Managing continuous queries in the presence of subqueries

Abstract
Techniques for managing continuous queries that include subqueries are provided. In some examples, a continuous query that includes at least a subquery may be identified. Additionally, the subquery may be processed to obtain a first result or generate a logical subquery plan. Further, in some instances, the continuous query may then be processed based at least in part on the first result from the subquery or by merging a logical continuous query plan with the logical subquery plan. This may result in obtaining a second result via querying a data source with the continuous query that is based at least in part on the first result from the subquery and/or the merged plans.
Description
BACKGROUND

In traditional database systems, data is stored in one or more databases usually in the form of tables. The stored data is then queried and manipulated using a data management language such as a structured query language (SQL). For example, a SQL query may be defined and executed to identify relevant data from the data stored in the database. A SQL query is thus executed on a finite set of data stored in the database. Further, when a SQL query is executed, it is executed once on the finite data set and produces a finite static result. Databases are thus best equipped to run queries over finite stored data sets.


A number of modern applications and systems however generate data in the form of continuous data or event streams instead of a finite data set. Examples of such applications include but are not limited to sensor data applications, financial tickers, network performance measuring tools (e.g. network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like. Such applications have given rise to a need for a new breed of applications that can process the data streams. For example, a temperature sensor may be configured to send out temperature readings.


Managing and processing data for these types of event stream-based applications involves building data management and querying capabilities with a strong temporal focus. A different kind of querying mechanism is needed that comprises long-running queries over continuous unbounded sets of data. While some vendors now offer product suites geared towards event streams processing, these product offerings still lack the processing flexibility required for handling today's events processing needs.


BRIEF SUMMARY

Techniques for providing continuous queries in the presence of subqueries are provided. According to at least one example, a computing system may identify a continuous query that includes at least a subquery. In some cases, identifying the continuous query may include receiving the continuous query or generating the continuous query. The computing system may also process the subquery to obtain a logical plan. The computing system may also process the continuous query based at least in part on merging the continuous query plan with the logical plan to obtain a result (e.g., the query result). In some examples, the continuous query may be configured to process business event data of a stream, business event data of a relation associated with the stream, and/or business event data of a database. Additionally, in some aspects, the relation may be configured as an unordered, time-varying set of tuples associated with the a stream of business event data. Additionally, in some examples, the computing system may provide the result to a user interface of a user that provided the continuous query. The user interface may be configured to display real-time data based at least in part on the result. Additionally, in some examples, the continuous query may be dependent on a first result from the subquery. The subquery may be configured as a continuous subquery and may be included within a “from” clause or a “set” clause of the continuous query. the subquery may also be configured to obtain a set of first results over time, and less than all of the set of first results may be stored in memory and accessible for processing the continuous query to obtain the result. Further, in some cases, the computing system may also reprocess the subquery to obtain a second logical plan over time based at least in part on an indication that data associated with the subquery has changed.


According to at least one example, a computer-readable memory may store instructions that, when executed by one or more processors, may cause the one or more processors to receive a continuous query statement with at least one nested subquery statement from a user associated with business event data. Additionally, the instructions may also cause the one or more processors to process the at least one nested subquery statement to obtain at least a logical subquery plan corresponding to the business event data. The instructions may also cause the one or more processors to process the continuous query based at least in part on merging a continuous query plan with the logical subquery plan to obtain at least a second result corresponding to the business event data. In some examples, the nested subquery may include at least another subquery. The data associated with the logical subquery plan may be included in a dimension table upon which the continuous query depends. Additionally, in some examples, the instructions may cause the one or more processors to receive an exception when data of the dimension table changes. Further, the dimension table may be refreshed by re-processing the at least one nested subquery statement based at least in part on the exception.


According to at least one example, a computer-implemented method may include receiving a continuous query statement from a user associated with business event data. The method may also include determining whether the continuous query includes one or more continuous subqueries. In some examples, when the method determines that the continuous query includes one or more subqueries, the method may include processing the continuous subquery to obtain a set of first results based at least in part on implementing a clause of the continuous subquery on a stream associated with the business event data of the user and processing the continuous query by utilizing at least a subset of the set of first results to obtain second results based at least in part on implementing a clause of the continuous query on the stream associated with the business event data of the user. In some aspects, the method may also include not instantiating an operator of the continuous query when it relies on a dimension table until a time after receiving an indication that data in the dimension table has changed. The method may also include loading less than all of the set of first results in memory and/or loading only a subset of the first results in memory when the subset is associated with a low probability of changing. Further, the stream associated with the business event data of the user may include one or more archived relations.


The foregoing, together with other features and embodiments, will become more apparent upon referring to the following specification, claims, and accompanying drawings.





BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the FIG. in which the reference number first appears. The use of the same reference numbers in different FIGS. indicates similar or identical items.



FIG. 1 is a simplified block diagram illustrating an example architecture for managing subquery and/or chaining techniques associated with continuous queries, according to at least one example.



FIG. 2 is a simplified block diagram illustrating at least some features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 3 is a simplified flow diagram illustrating at least some additional features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 4 is a simplified flow diagram illustrating at least some additional features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 5 is a simplified process flow illustrating at least some features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 6 is another simplified process flow illustrating at least some features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 7 is another simplified process flow illustrating at least some features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 8 is a simplified process flow illustrating at least some features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 9 is another simplified process flow illustrating at least some features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 10 is another simplified process flow illustrating at least some features of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 11 is a simplified block diagram illustrating components of a system environment that may be used in accordance with an embodiment of the management of subquery and/or chaining techniques associated with continuous queries described herein, according to at least one example.



FIG. 12 is a simplified block diagram illustrating a computer system that may be used in accordance with embodiments of the management of subquery and/or chaining techniques associated with continuous queries described herein described herein, according to at least one example.





DETAILED DESCRIPTION

In the following description, various embodiments will be described. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the embodiments. However, it will also be apparent to one skilled in the art that the embodiments may be practiced without the specific details. Furthermore, well-known features may be omitted or simplified in order not to obscure the embodiment being described.


In some examples, mechanisms to support continuous query language (CQL) queries (also referred to as “query statements”) with one or more continuous subqueries, for example, including but not limited to, nested subqueries, subqueries that operate over a time interval, subqueries that query streaming or relation data, etc., may be provided For example, in some scenarios, a query may rely on a subquery that collects data from a stream, relation, or archived relation. The query may then run utilizing the results of the subquery. Additionally, in some examples, mechanisms to support chaining (also referred to as “daisy chaining”) of queries and/or data objects (DOs) may be provided. For example, a continuous query may collect data from a stream or relation and store that data in a data object. The data object may be updatable, analyzed, and/or displayed. Additional audits may be performed on the data object. Additionally, in some examples, additional continuous queries may rely on the DO.


A continuous data stream (also referred to as an event stream) may include a stream of data or events that may be continuous or unbounded in nature with no explicit end. Logically, an event or data stream may be a sequence of data elements (also referred to as events), each data element having an associated timestamp. A continuous event stream may be logically represented as a bag or set of elements (s, T), where “s” represents the data portion, and “T” is in the time domain. The “s” portion is generally referred to as a tuple or event. An event stream may thus be a sequence of time-stamped tuples or events.


In some aspects, the timestamps associated with events in a stream may equate to a clock time. In other examples, however, the time associated with events in an event stream may be defined by the application domain and may not correspond to clock time but may, for example, be represented by sequence numbers instead. Accordingly, the time information associated with an event in an event stream may be represented by a number, a timestamp, or any other information that represents a notion of time. For a system receiving an input event stream, the events arrive at the system in the order of increasing timestamps. There could be more than one event with the same timestamp.


In some examples, an event in an event stream may represent an occurrence of some worldly event (e.g., when a temperature sensor changed value to a new value, when the price of a stock symbol changed) and the time information associated with the event may indicate when the worldly event represented by the data stream event occurred.


For events received via an event stream, the time information associated with an event may be used to ensure that the events in the event stream arrive in the order of increasing timestamp values. This may enable events received in the event stream to be ordered based upon their associated time information. In order to enable this ordering, timestamps may be associated with events in an event stream in a non-decreasing manner such that a later-generated event has a later timestamp than an earlier-generated event. As another example, if sequence numbers are being used as time information, then the sequence number associated with a later-generated event may be greater than the sequence number associated with an earlier-generated event. In some examples, multiple events may be associated with the same timestamp or sequence number, for example, when the worldly events represented by the data stream events occur at the same time. Events belonging to the same event stream may generally be processed in the order imposed on the events by the associated time information, with earlier events being processed prior to later events.


The time information (e.g., timestamps) associated with an event in an event stream may be set by the source of the stream or alternatively may be set by the system receiving the stream. For example, in certain embodiments, a heartbeat may be maintained on a system receiving an event stream, and the time associated with an event may be based upon a time of arrival of the event at the system as measured by the heartbeat. It is possible for two events in an event stream to have the same time information. It is to be noted that while timestamp ordering requirement is specific to one event stream, events of different streams could be arbitrarily interleaved.


An event stream has an associated schema “S,” the schema comprising time information and a set of one or more named attributes. All events that belong to a particular event stream conform to the schema associated with that particular event stream. Accordingly, for an event stream (s, T), the event stream may have a schema ‘S’ as (<time stamp>, <attribute(s)>), where <attributes> represents the data portion of the schema and can comprise one or more attributes. For example, the schema for a stock ticker event stream may comprise attributes <stock symbol>, and <stock price>. Each event received via such a stream will have a time stamp and the two attributes. For example, the stock ticker event stream may receive the following events and associated timestamps:
















...



(<timestamp_N>, <NVDA,4>)



(<timestamp_N+1>, <ORCL,62>)



(<timestamp_N+2>, <PCAR,38>)



(<timestamp_N+3>, <SPOT,53>)



(<timestamp_N+4>, <PDCO,44>)



(<timestamp_N+5>, <PTEN,50>)



...










In the above stream, for stream element (<timestamp N+1>, <ORCL,62>), the event is <ORCL,62> with attributes “stock_symbol” and “stock_value.” The timestamp associated with the stream element is “timestamp N+1”. A continuous event stream is thus a flow of events, each event having the same series of attributes.


As noted, a stream may be the principle source of data that CQL queries may act on. A stream S may be a bag (also referred to as a “multi-set”) of elements (s, T), where “s” is in the schema of S and “T” is in the time domain. Additionally, stream elements may be tuple-timestamp pairs, which can be represented as a sequence of timestamped tuple insertions. In other words, a stream may be a sequence of timestamped tuples. In some cases, there may be more than one tuple with the same timestamp. And, the tuples of an input stream may be requested to arrive at the system in order of increasing timestamps. Alternatively, a relation (also referred to as a “time varying relation,” and not to be confused with “relational data,” which may include data from a relational database) may be a mapping from the time domain to an unbounded bag of tuples of the schema R. In some examples, a relation may be an unordered, time-varying bag of tuples (i.e., an instantaneous relation). In some cases, at each instance of time, a relation may be a bounded set. It can also be represented as a sequence of timestamped tuples that may include insertions, deletes, and/or updates to capture the changing state of the relation. Similar to streams, a relation may have a fixed schema to which each tuple of the relation may conform. Further, as used herein, a continuous query may generally be capable of processing data of (i.e., queried against) a stream and/or a relation. Additionally, the relation may reference data of the stream.


In some examples, business intelligence (BI) may help drive and optimize business operations at particular intervals (e.g., on a daily basis in some cases). This type of BI is usually called operational business intelligence, real-time business intelligence, or operational intelligence (OI). Operational Intelligence, in some examples, blurs the line between BI and business activity monitoring (BAM). For example, BI may be focused on periodic queries of historic data. As such, it may have a backward-looking focus. However, BI may also be placed into operational applications, and it may therefor expand from a mere strategic analytical tool into the front lines in business operations. As such, BI systems may also be configured to analyze event streams and compute aggregates in real time.


In some examples, a continuous query language service (CQ Service) may be configured to extend a BI analytics server to handle continuous queries and enable real-time alerts. The CQ Service, in some aspects, may provide integration with a BI analytics server and a CQL engine. By way of example only, a BI analytics server may delegate continuous queries to the CQServiceand the CQServicemay also act as a logical database (DB) gateway for a CQL engine. In this way, the CQL engine may be able to leverage the BI analytics server for its analytics capabilities and semantic modeling. In some examples, the CQL engine may be wrapped inside the CQ Service.


In some examples, the CQService may provide, among other things, the following functionalities:

    • Remoting service for BI Analytics Server as CQL engine Gateway;
    • Event source/sink adapter;
    • Generate data definition languages (DDLs) from logical SQL plus CQL extensions;
    • Provide unified model for all types of continuous queries and implementation selections;
    • Maintain metadata and support restartability; and
    • High availability and scalability support.


Additionally, in some examples, OI is a form of real-time dynamic, business analytics that can deliver visibility and insight into business operations. OI is often linked to or compared with BI or real-time BI, in the sense that both help make sense out of large amounts of information. But there are some basic differences: OI may be primarily activity-centric, whereas BI may be primarily data-centric. Additionally, OI may be more appropriate for detecting and responding to a developing situation (e.g., trend and pattern), unlike BI which may traditionally be used as an after-the-fact and report-based approach to identifying patterns.


In some examples, a business event analysis and monitoring (BEAM) system may include a CQL engine to process and/or receive in-flight data. For example, a CQL engine may be an in-memory database engine configured to query or otherwise process incoming real-time information (e.g., BI or OI). The CQL engine may utilize or understand temporal semantics and be configured to allow definition of a window of data to process. Utilizing a CQL engine may, in some cases, involve always running a query on incoming data.


In some aspects, the CQL engine may include a full blown query language. As such, a user may specify computations in terms of a query. Additionally, the CQL engine may be designed for optimizing memory, utilizing query language features, operator sharing, rich pattern matching, rich language constructs, etc. Additionally, in some examples, the CQL engine may process both historical data and streaming data. For example, a user can set a query to send an alert when California sales hit above a certain target. Thus, in some examples, the alert may be based at least in part on historical sales data as well as incoming live (i.e., real-time) sales data.


In some examples, the CQL engine or other features of the below described concepts may be configured to combine a historical context (i.e., warehouse data) with incoming data in a real-time fashion. Thus, in some cases, the present disclosure may describe the boundary of database stored information and in-flight information. Both the database stored information and the inflight information may include BI data. As such, the database may, in some examples, be a BI server or it may be any type of database. Further, in some examples, the features of the present disclosure may enable the implementation of the above features without users knowing how to program or otherwise write code. In other words, the features may be provided in a feature-rich user interface (UI) or other manner that allows non-developers to implement the combination of historical data with real-time data.


Additionally, in some examples, the present disclosure may describe dashboard customization and/or personalization. A CEP engine may be configured to include advanced, continuous analysis of real-time information and historical data. Business process models (BPMs) may include performing model-driven execution of policies and processes defined as BPM notation (BPMN) models. Key result indicators (KRI) may be utilized to tell a user how they have done in a perspective or critical success factor (CSF). For example, it may provide results for many actions, it may cover a longer period of time than key performance indicators (KPIs), and/or it may be reviewed on monthly or quarterly periods. Result indicators (RIs) may be utilized to tell a user what they have done. For example, it may summarize activity, and financial performance measure and/or it may update daily, weekly, or monthly. Further, in some aspects, performance indicators (PIs) may be utilized to inform a user what actions to take or at least make recommendations. Additionally, it may include non-financial information and may, in some cases, complement the KPI.


In some aspects, PI may be reviewed 24/7, daily, weekly, or less regularly. In some cases, KPI may include a set of measures that are most critical for the current and future success of an organization. Some KPIs may be updated daily or even 24/7 while the rest of the information may be reported weekly. Examples of KPI notifications may include, but are not limited to, whether a plane or other service vehicle was delayed or whether a trailer has been sent out underweight the previous day for a distribution company (e.g., to discover better utilization of the trucks).


In some examples, embodiments for managing real-time business events may include integrating (e.g., seamlessly) business activity monitoring, complex event processing, and business intelligence to provide a complex, and real-time set of operational information. Additionally, continuous monitoring of business events may be utilized to gain real-time visibility of business processes and/or workflows. In some examples, OI may be supplemented with traditional business intelligence. As such, operational intelligence may give more insight into business operations versus BI, which, as noted above, is more data centric. For example, OI may get inside to determine how a business is doing in a real-time fashion. Whereas BI may be more akin to data warehousing (e.g., indicating information after the fact).


Examples of KPI may include real-time call processing time. For example, a user may set real time KPI to be 15 minutes, versus weeks or days. As such, users may be enabled to take actions right away. Further, by coupling historical (data centric) information from BI warehouses with current real-time data, users may be able to view how a business is running in the current state (including continuously updated, streaming data). In some examples, advanced continuous analysis of real-time information may be included in the data processing. Additionally, incremental computations may be performed and included in displays, visualizations, user interfaces (UIs), etc.


In some examples, subqueries may be supported. Additionally, the CQL and/or CQL engine may support nested and/or other types of query aggregation and may provide incremental computation. Further, in some examples a slow changing dimension table may be utilized; yet, the CQL engine may still perform efficient processing. In one example, while joining a FACT table with a slow changing dimension table, one or more join operators may not be instantiated. In this way, memory utilization may be greatly reduced.


In some aspects, incremental computation may include not bringing all of a relational source (i.e., warehouse data) and/or streaming data into memory for a particular query. For example, for certain dimensions (e.g., a data source associated with a software product) utilized or referenced by the query, the data may not change very often (e.g., once a month or so). As such, there may be no need to load the entire source into memory for every query. So, in some cases, the data may be imported as a dimension table. Then, whenever a change happens to the dimension table, an exception may be thrown at runtime. In some cases, the exception may be received by the CQService and processed. Based at least in part on an indication that the exception is known and understood, the CQService may then reissue the query. In other words, the exception may be formatted as a special exception that indicates to the CQService that the query should be reissued in order to take the change in the dimension into account. Otherwise, other dependent factors, streams, data, or query tree branches may not be accurate or synchronous with the data of the dimension table. In some examples, the subquery may be a continuous subquery configured to be queried against a stream or a relation.


In some examples, based at least in part on a query tree, a parent operator (e.g., join or some other operator that may depend on data from multiple other operators or branches of the tree) may be responsible for starting the generation and/or execution of the query. That is, the stateful operator may initialize the state by loading appropriate relational data, streaming data, or relation data. Additionally, the parent operator may delegate to one of the child operators but not to the other (based at least in part on which operators include dimension tables). Further, once it is known that one of the operators includes a dimension (e.g., based at least in part on metadata associated with the relational source that indicates that it is a dimension), the parent operator may be configured to listen for changes to the dimension table. As indicated, when changes in the dimension table are found, an exception or other indication may be provided to inform the CQService to restart the query. Alternatively, the parent operator may be directly informed of the dimension table change; thus, signaling that it should reissue and/or re-initialize the query.


Additionally, in some examples, query and/or DO chaining may be provided. A Write Back DO may be a specialized Data Object and it can be configured with persistence. It may be used to store output events of a CQL query so it can be analyzed/audited or it can be used in a daisy chain manner for another CQL to sit on top. In some examples, a first query may run against a DO and insert the output events into the Write Back DO; then, a user can examine the Write Back DO for audit purposes, map it to a visualization, or author another query against the write back DO.


In some aspects, a write back DO may be used for chaining queries or binding to visualizations. A write back DO may be either a (archived) stream/archived relation DO. Additionally, in some examples, a complex query with subqueries in it can be broken up into two separate queries. A daisy chain query can be achieved by:


(Q1→ WB DO1→Q2→WB DO2)

    • In this example, query 1 collects data based at least in part on moving window (e.g., moving average execution time for the last 60 minutes, 30 minutes, or the like) on a time interval basis (e.g., every 10 minutes, 20 minutes, or the like).
    • The output of the query may go into DO1, and a change data control (CDC) may kick in and send the delta into CQL Engine for Q2 to consume (e.g., pattern detection).
    • If Q2 is a running pattern match query (i.e., the trends continue to go up by 10% each time for at least some number of time in a row), it may output an alert to the operator.


      With this approach, Q1 does not need to keep events in memory; instead, it may be configured as a tactical query which may run every few minutes (e.g., 10 or more).


In some examples, a continuous query may be registered once and it may run for a long period of time (at least until instructed to end). This continuous query may receive incoming events and perform computations in memory (e.g., pattern match, aggregation function deltas, moving average computation, etc.). It may also have the notion of range (moving window) and slide (output throttles). For example:














   SELECT customerLocation Id,


     customerStatus,


     MAX(callProcessingtime) AS MAXcallProcessingTime


     FROM CALLCENTER_TEST1.-


CALLCENTER_FACT[RANGE 60 minute ON callClosedTime


SLIDE 10 minute]









As the range increases, the memory footprint may become large as it has to keep all these events in memory. The events may expire when they exceed the range size. As the number of queries increases, the memory footprint may become even bigger. For at least these reasons, leveraging the write back DO and chaining queries may optimize memory usage.


The techniques described above and below may be implemented in a number of ways and in a number of contexts. Several example implementations and contexts are provided with reference to the following figures, as described below in more detail. However, the following implementations and contexts are but a few of many.



FIG. 1 depicts a simplified example system or architecture 100 in which techniques for managing subqueries and/or query chaining within a CQL may be implemented. In architecture 100, one or more users 102 (e.g., account holders) may utilize user computing devices 104(1)-(N) (collectively, “user devices 104”) to access one or more service provider computers 106 via one or more networks 108. In some aspects, the service provider computers 106 may also be in communication with one or more streaming data source computers 110 and/or one or more databases 112 via the networks 108. For example, the users 102 may utilize the service provider computers 106 to access or otherwise manage data of the streaming data source computers 110 and/or the databases 112 (e.g., queries may be run against either or both of 110, 112). The databases 112 may be relational databases, SQL servers, or the like and may, in some examples, manage historical data, event data, relations, archived relations, or the like on behalf of the users 102. Additionally, the databases 112 may receive or otherwise store data provided by the streaming data source computers 110. In some examples, the users 102 may utilize the user devices 104 to interact with the service provider computers 106 by providing queries (also referred to as “query statements”) or other requests for data (e.g., historical event data, streaming event data, etc.). Such queries or requests may then be executed by the service provider computers 106 to process data of the databases 112 and/or incoming data from the streaming data source computers 110. Further, in some examples, the streaming data source computers 110 and/or the databases 112 may be part of an integrated, distributed environment associated with the service provider computers 106.


In some examples, the networks 108 may include any one or a combination of multiple different types of networks, such as cable networks, the Internet, wireless networks, cellular networks, intranet systems, and/or other private and/or public networks. While the illustrated example represents the users 102 accessing the service provider computers 106 over the networks 108, the described techniques may equally apply in instances where the users 102 interact with one or more service provider computers 106 via the one or more user devices 104 over a landline phone, via a kiosk, or in any other manner. It is also noted that the described techniques may apply in other client/server arrangements (e.g., set-top boxes, etc.), as well as in non-client/server arrangements (e.g., locally stored applications, etc.).


The user devices 104 may be any type of computing device such as, but not limited to, a mobile phone, a smart phone, a personal digital assistant (PDA), a laptop computer, a desktop computer, a thin-client device, a tablet PC, etc. In some examples, the user devices 104 may be in communication with the service provider computers 106 via the networks 108, or via other network connections. Further, the user devices 104 may also be configured to provide one or more queries or query statements for requesting data of the databases 112 (or other data stores) to be processed.


In some aspects, the service provider computers 106 may also be any type of computing devices such as, but not limited to, mobile, desktop, thin-client, and/or cloud computing devices, such as servers. In some examples, the service provider computers 106 may be in communication with the user devices 104 via the networks 108, or via other network connections. The service provider computers 106 may include one or more servers, perhaps arranged in a cluster, as a server farm, or as individual servers not associated with one another. These servers may be configured to perform or otherwise host features described herein including, but not limited to, the fast path evaluation of Boolean predicates described herein. Additionally, in some aspects, the service provider computers 106 may be configured as part of an integrated, distributed computing environment that includes the streaming data source computers 110 and/or the databases 112.


In one illustrative configuration, the service provider computers 106 may include at least one memory 136 and one or more processing units (or processor(s)) 138. The processor(s) 138 may be implemented as appropriate in hardware, computer-executable instructions, firmware, or combinations thereof. Computer-executable instruction or firmware implementations of the processor(s) 138 may include computer-executable or machine-executable instructions written in any suitable programming language to perform the various functions described.


The memory 136 may store program instructions that are loadable and executable on the processor(s) 138, as well as data generated during the execution of these programs. Depending on the configuration and type of service provider computers 106, the memory 136 may be volatile (such as random access memory (RAM)) and/or non-volatile (such as read-only memory (ROM), flash memory, etc.). The service provider computers 106 or servers may also include additional storage 140, which may include removable storage and/or non-removable storage. The additional storage 140 may include, but is not limited to, magnetic storage, optical disks, and/or tape storage. The disk drives and their associated computer-readable media may provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for the computing devices. In some implementations, the memory 136 may include multiple different types of memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), or ROM.


The memory 136, the additional storage 140, both removable and non-removable, are all examples of computer-readable storage media. For example, computer-readable storage media may include volatile or non-volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. The memory 136 and the additional storage 140 are all examples of computer storage media.


The service provider computers 106 may also contain communications connection(s) 142 that allow the identity interface computers 120 to communicate with a stored database, another computing device or server, user terminals, and/or other devices on the networks 108. The service provider computers 106 may also include input/output (I/O) device(s) 144, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, a display, one or more speakers, a printer, etc.


Turning to the contents of the memory 136 in more detail, the memory 136 may include an operating system 146 and one or more application programs or services for implementing the features disclosed herein including at least a subquery module 148 and/or a query chain module 149. As used herein, modules may refer to programming modules executed by servers or clusters of servers that are part of a service. In this particular context, the modules may be executed by the servers or clusters of servers that are part of the service provider computers 106. In some examples, the subquery module 148 may be configured to, receive, identify, generate or otherwise provide one or more continuous queries 150 that may contain subqueries 152, 154 (e.g., continuous and/or tactical subqueries). For example, a continuous query 150 (e.g., a query configured to be run against a stream or relation) may include one or more subqueries 152, 154 or nested subqueries 154 upon which the query sits (i.e., on which it depends). More specifically, a continuous query 150 may include a subquery 152 which may in turn include a subquery 154 (e.g., nested within the first subquery 152). Other scenarios are possible, as desired, for example, the continuous query 150 may include two or more subqueries 152 with no nested subqueries 154, or the like.


In some examples, a CQL engine in may support nesting and/or embedding of one or more queries inside another via the mechanism of views. For example, the following CQL code may be utilized to configure such a query that includes a subquery:














create view sales_v1 as select prodid, sales as sales from


sales_stream [RANGE 24 hours];


create query q0 as select prodid, sum(sales) from sales_v1 group by


prodid;









In some aspects, this approach may provide modularity and reuse. Additionally, it may also create a dependency for query q0 on the view sales_v1. So the definition of view sales_v1 may, in some cases, not be changed as long as there are dependent continuous queries on it. Even for cases where the new view definition is congruent (i.e. say project list does not change in number of items, data types, and position of items), it may request replacing of each of the queries dynamically. Alternatively, or in addition, for set queries, non-standard notation may be utilized (e.g., <view> UNION <view>). This type of syntax may not be ANSI compliant; however, the CQServiceand/or CQ Engine may be configured to process it. Further, in some aspects, some queries may be generated through a sequence of workflow steps and/or destroyed on the fly. In such cases, the view mechanism may not actually be feasible, as it requests that the clients know the dependencies.


Support for nested subqueries (also known as inline queries or sub-select) may be implemented by specifying the subquery in the FROM clauses of a query where sources relations/streams are specified. Subquery support will also be extended to set operation queries. The following sets of CQL code illustrate at least three non-limiting examples:


Example 1

This example shows a select-from-where (SFW) query embedded inside another SFW query:
















CREATE QUERY q0 AS



SELECT prodid, sum(sales)



FROM (SELECT prodid AS prodid, sales AS sales FROM



sales_stream [RANGE 24 HOURS]) AS foo



GROUP BY prodid;









Example 2

This example shows a subquery with a set of operations. With subquery feature, queries that define views can be specified inline as operands to the set operations as follows (as opposed to as <view1> UNION ALL <view2>):
















(SELECT c1, c2, c3 FROM S0 [RANGE 5 HOURS])



UNION ALL



(SELECT c1, c2, c3 FROM S1[RANGE 5 HOURS])









Example 3

This query shows how an SFW query, set operation query, and subquery can be combined in a powerful way to compose a query:
















CREATE QUERY q0 AS



SELECT *



FROM



(



  (SELECT c1, c2, c3 FROM S0 [RANGE 5 HOURS])



  UNION ALL



  (SELECT c1, c2, c3 FROM S1[RANGE 5 HOURS])



) AS foo



GROUP BY c1



ORDER BY c1









In some examples, each item in a SELECT clause (or project list) of a subquery containing expressions may be explicitly aliased. This is similar to having view definitions where a project list has expressions. Expressions as such may not have names, so it may be useful to name or alias them explicitly using <expr> AS <name>. Additionally, it may not be necessary to alias a project list in SELECT*FROM <source> or SELECT <sourcealias>.* FROM <source> AS sourcealias or SELECT c1, c2, c3 FROM <source> where ci refers to an attribute of the source (which can be a base stream, relation, or another subquery). This may either be derived implicitly (in case of * notation) or may be trivially obvious when each expression refers only to base attributes. For an SFW query (query contain select-from-where), the subquery itself may also be aliased. However, in some examples, it may be an error not to specify an explicit alias. For set operations, in some cases, the subquery may also not be aliased. However project items with expressions may be requested to be aliased. Names of the select items of the left query may serve as the name of the overall set operation. In some examples, subqueries may only be supported in FROM clauses of a query. However, in other examples, the subqueries may be supported in any clause of the query. Further, in some cases, there is no theoretical limit on the depth of nesting. However the amount of nesting may be affected by the amount available memory of the host computing system.


Further, in some examples, an SFW query may be a very comprehensive construct with many possible clauses and combinations thereof. Each of these clauses can refer to the “attributes” of a subquery much the same way they do for a relation and stream (e.g., in the from clause). Clauses that may support subqueries include, but are not limited to, GROUP BY, ORDER BY, MATCH_RECOGNIZE, JOIN, SELECT, WHERE, ARCHIVED RELATION, and/or VALUE WINDOW.


Additionally, in some examples, the query conversion module 149 may be configured to enable query chains and/or query aggregations. For example, a query chain 156 may include a first query (e.g., a continuous query) 158, a data object (e.g., a Write Back DO) 160, and/or a second query (e.g., another continuous query) 162). As noted above, in some aspects, the first query 158 may be queried against a stream, relation, or database, and may also store the results in the data object 160. Subsequent queries (e.g., the second query 162) may then be queried against the DO 160 to obtain a second result.


As noted above, in some examples, a Write Back Data Object may be a specialized DO and it can be configured with persistence. It may be utilized to store output events of a CQL query (e.g., the first query 158) so it can be analyzed and/or audited, or it can be utilized in a daisy chain manner for another CQL query (e.g., the second query 162) to sit on top. The first query 158 may run against an initial DO (e.g., a stream, a relation, a database, another data construct, etc.) and may insert the output events into the Write Back DO 160; then a user can examine the Write Back DO 160 for audit purposes, map it to a visualization, or choose to author another query 162 against the write back DO 160.


In at least one non-limiting example, there may be a Performance DO. Additionally, a query may be written against the Performance DO to compute the moving average processing time for a particular type of process at a certain interval and output the results into another Write Back DO “Avg Processing Time.” Now a bar chart can be constructed against this DO to show the historical value. Alternatively, or in addition, another pattern match query can be written against this Write Back DO to perform trend analysis. For trend analysis queries, the first and foremost requirement may be that the data source be a stream data source which may be insert only, to which the Write Back DO belongs. Thus, in some cases, the Write Back DO may always be a stream DO (i.e. only insert, no delete, upsert, or update) and can be configured with either persistence or no persistence. When persistence is not configured, no flex table is created and the “insert” event may be processed by Persistence in memory and pass through to CQ Service.


A user 102 with a data architect role may be able to create a Write Back DO. The Write Back DO may be surfaced in a Continuous Query template or the like. The user 102 may also be allowed to choose a Write Back Data Object as an optional step. The user 102 may also be able to map the select list to the Write Back DO and/or modify the Write Back DO just like with most other DO. After a Write Back DO is defined, when the CQL query fires, the output event may be sent to Persistence via a java messaging service (JMS) tool or the like. In some cases it may leverage the JMS adapter application programming interface (API) to send out the insert event to a Persistence tool of the service provider computers 106.


In some examples, a significant performance savings can be gained by converting a complex CQL query into two separate queries with a Write Back DO in between. For example, note that in the following subquery portion, the query is running a continuous query (e.g., with an archived relation) which computes the max call processing time for the calls which were closed in the last (moving) 60 minutes and output the result every 10 minutes. In this query, all events in the last 60 minutes are stored in memory and they expired individually as time moves on. So if you have 20,000 events come in every 60 minutes, CQL Engine will store 20,000 events in memory at any given time and the max processing time is being re-computed every time an event enters the system. Finally, at every 10 minutes interval, the max processing time is being outputted as an insert stream. Additionally, in the second part of the query, note that the query is taking in the output from the subquery and it performs a pattern match where it's detecting an upward trending (the current measure is 7% more than last measure and this pattern has been detected for 7 times in a row).














CREATE QUERY CALLCENTER_TEST1.trendingQuery1 as


SELECT T.customerLocationId ,


  T.customerStatus ,


  T.MAXcallProcessingTime


FROM (


ISTREAM(


 SELECT customerLocationId ,


     customerStatus ,


    MAX(callProcessingTime) AS MAXcallProcessingTime


 FROM CALLCENTER_TEST1.CALLCENTER_FACT[RANGE


60 minute ON callClosedTime SLIDE 10 minute]


 WHERE customerLocationId = ‘CN’


GROUP BY customerLocationId


)


) AS q


MATCH_RECOGNIZE (


 MEASURES


  A.customerLocationId AS customerLocationId,


  A.customerStatus AS customerStatus,C.MAXcallProcessingTime AS


MAXcallProcessingTime


 ALL MATCHES


 PATTERN (A B+ C)


 DEFINE


  B AS B.MAXcallProcessingTime>0.07*prev(B.-


MAXcallProcessingTime) and count(*) < 7,


  C AS C.MAXcallProcessingTime>0.07*last(B.-


MAXcallProcessingTime) and count(*) = 7


) AS T destination “jms:topic/oracle.beam.cqs.activedata”









Note that there's a lot of memory being consumed by the first query. Instead with the Write Back DO and the CQL Scheduled Query, the following can be achieved (e.g., since the first query only outputs every 10 minutes, one can re-write the query):














CREATE QUERY CALLCENTER_TEST1.trendingQuery1 as


SELECT customerLocationId ,


    customerStatus ,


    MAX(callProcessingTime) AS MAXcallProcessingTime


 FROM CALLCENTER_TEST1.CALLCENTER_FACT WHERE


customerLocationId = ‘CN’ and


TIMESTAMPDIFF(SQL_TSI_MINUTE, callClosedTime,


CURRENT_TIMESTAMP) < 60


GROUP BY customerLocationId


REFRESH ON “0:0:0” AT EVERY 10 MINUTES


)









In this case, the CQL will run this query every 10 minutes and no memory is taken up while running this query. The output of this query then goes into a STREAM based Write Back DO. At that point, the second query (pattern match for trending) is then applied to this intermediate Write Back DO. With this approach, the only memory consumption is on the trending query which keeps track of the last 7 events from the Write Back DO. Additionally, a few examples of the operations of the subquery module 148, the query chain module 149, and/or the service provider computers 106 are described in greater detail below.


Additional types of computer storage media (which may also be non-transitory) that may be present in the service provider computers 106 and/or user devices 104 may include, but are not limited to, programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the service provider computers 106 and/or user devices 104. Combinations of any of the above should also be included within the scope of computer-readable media.


Alternatively, computer-readable communication media may include computer-readable instructions, program modules, or other data transmitted within a data signal, such as a carrier wave, or other transmission. However, as used herein, computer-readable storage media does not include computer-readable communication media.



FIG. 2 illustrates a simplified block diagram 200 with which features of the management of continuous queries in the presence of subqueries may be described. As noted above, in some examples, a subquery module 148 may be executed by the service provider computers 106 of FIG. 1 and may include one or more continuous queries 150 that rely on one or more subqueries 152. In one non-limiting example, the continuous query 150 may depend on results of the subquery 152. As such, the subquery may first query against a stream 202 that may be provided or otherwise managed by the streaming data source computers 110 of FIG. 1. However, in other examples, the subquery 152 may query against a relation, a data object, or a database (e.g., a relational database or the like). Additionally, in some examples, a logical plan may be generated or otherwise built based at least in part on the subquery 152. Once the subquery has at least one result, the continuous query 150 may query against the stream 202 utilizing the results of the subquery 152. In this way, data of the stream 202 or of another stream may be accessible without having knowledge of some of the actual keys. For example, the subquery 152 may retrieve a result that can be utilized by the continuous query 150 as a key for querying against the stream 202. Further, in some aspects, the logical (or physical plan) describing the subquery 152 may be merged (e.g., at a logical layer) with a plan that is based at least in part on the continuous query 150. For example, where the continuous query 150 (i.e., the parent query in this example) expects a FROM source (e.g., a stream, relation, etc.), the logical plan for implementing the subquery 152 may be included. As such, beyond the logical layer, it may be indistinguishable whether the continuous query 150 ever included any subqueries 152. In this way, may continuous queries 150 that include the same subqueries 152 may automatically share the same plan operators.


Further, as noted above, in some examples, a slow changing dimension table may be utilized (e.g., when the subquery 152 queries against a relational source). The relational source may provide historical and/or warehoused data as opposed to streaming data. As such, some of the data obtained by the subquery 152 may not change often. Yet, when it does, the continuous query 150 may request that the subquery 152 be re-implemented in order to update or otherwise refresh the result that the continuous query 150 is relying upon.



FIG. 3 depicts a simplified flow diagram showing one or more techniques 300 for implementing the management of continuous queries in the presence of subqueries, according to one example. In FIG. 3, the service provider computers 106 are again shown in communication with the users 102 and/or user devices 104 via the networks 108. Additionally, in some examples, the service provider computers 106 may include or be in communication with (e.g., via the networks 108) one or more stream/relation computers 302. While techniques 300 are shown in FIG. 3 in a particular order (including arbitrary sequence numbers), it should be understood that no particular order is necessary and that one or more steps or parts of the techniques 300 may be omitted, skipped, and/or reordered. In at least one non-limiting example, the one or more service provider computers 106 described above with reference to FIGS. 1 and 2 may receive a continuous query with a subquery from the user devices 104. The continuous query may be configured to request processing (e.g., retrieval, storage, deletion, etc.) of database data (e.g., data stored in a database), streaming event data (e.g., data being received in real-time from the stream/relation computers 302), and/or relation data (e.g., relations received from the stream/relation computers 302).


Additionally, in some examples, the service provider computers 106 may also process the subquery found within the continuous query by querying it against a relation or a stream of the stream/relation computers 302. However, in other examples, processing the subquery may include generating a logical and/or physical plan for implementing the subquery. In this way, the subquery itself may not be processed against a data source until the continuous query is processed. When a logical plan is generated, the logical plan may then be merged with a plan generated to implement the continuous query. For example, as noted above, the logical plan may be included at the FROM statement of the continuous query. In response, the service provider computers 106 may receive data based at least in part on the subquery or the data associated with the subquery may be received after merger, when the continuous query is applied against the data source (e.g., relation, stream, or other source). As noted, the continuous query may then be applied against a relation or stream, but including the results received via the subquery. As such, the continuous query may be queried against the stream or relation of the stream/relation computers 302 based at least in part on the subquery results. Additionally, in some examples, the service provider computers 106 may then receive data from the stream/relation computers 302 based at least in part on the continuous query parameters and the subquery result. Further, the service provider computers 106 may then provide the result to the user devices 104. Additionally, alerts may also be provided to the user devices 104 and/or visualization information.



FIG. 4 illustrates a simplified block diagram 400 with which features of the mechanisms for chaining continuous queries may be described. As noted above, in some examples, a query chain module 149 may be executed by the service provider computers 106 of FIG. 1 and may include one or more continuous queries 402, 404 and/or one or more data objects 406. In one non-limiting example, a second continuous query 404 may depend on or query against a data object 406. However, the DO 406 may contain results obtained via a first continuous query 402. As such, the first continuous query 402 may first query against a stream 408 that may be provided or otherwise managed by the streaming data source computers 110 of FIG. 1. However, in other examples, the first continuous query 402 may query against a relation, a data object, or a database (e.g., a relational database or the like). Once the first continuous query 402 has at least one result, that data may be stored in the DO 406. In some examples, the DO 406 may be a stream DO (e.g., only utilizing insert clauses) or it may be any type of DO.


Once the data of collected (i.e., obtained) by the first continuous query 402 is stored in the DO 406, a user or other entity may access the DO 406. For example, the data of the DO 406 may be audited, displayed, edited, or otherwise managed. As such, the data of the DO 406 may be provided to a user interface 410. Additionally, subsequent queries (e.g., the second continuous query 404) may later query against the DO 406. In this way, multiple continuous queries (or tactical queries) may be chained together, where subsequent queries rely or otherwise depend upon previous queries via DOs 406.



FIGS. 5-7 illustrate example flow diagrams showing respective processes 500, 600, and 700 for implementing the management of continuous queries in the presence of subqueries described herein. These processes 500, 600, 700 are illustrated as logical flow diagrams, each operation of which represents a sequence of operations that can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures and the like that perform particular functions or implement particular data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement the processes.


Additionally, some, any, or all of the processes may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable storage medium may be non-transitory.


In some examples, the one or more service provider computers 106 (e.g., utilizing at least the subquery module 148 of FIG. 1) shown in FIGS. 1-3 may perform the process 500 of FIG. 5. The process 500 may begin by including identifying a continuous query that includes at least one subquery (e.g., a continuous subquery or a tactical subquery) at 502. In some examples, the process 500 may also include, at 504, processing the subquery to obtain a logical plan for implementing the subquery (e.g., the logical plan may include steps for querying against a data source with the subquery). At 506, the process 500 may also include processing the continuous query (e.g., the query that includes the subquery) based at least in part on merging the logical plan with a continuous query logical plan. For example, at the FROM statement of the continuous query (or a logical plan for implementing the continuous query) the logical subquery plan may be included. In some aspects, this may include querying against a data source (e.g., the same data source that the subquery queried against or another data source) utilizing the first result from the subquery. At 508, the process 500 may include providing the second result to a user interface of the user. Further, the process 500 may end at 510 by including reprocessing the subquery based at least in part on an indication that data of the subquery has changed.



FIG. 6 illustrates an example flow diagram showing process 600 for implementing the management of continuous queries in the presence of subqueries described herein. The one or more service provider computers 106 (e.g., utilizing at least the subquery module 148 of FIG. 1) shown in FIGS. 1-3 may perform the process 600 of FIG. 6. The process 600 may begin at 602 by including receiving a continuous query statement that includes one or more nested subqueries. The continuous query with nested subqueries may, in some examples, be received from a user. At 604, the process 600 may include processing the nested subqueries to obtain a logical plan for implementing the nested subqueries (e.g., at least one of the nested subqueries and/or at least the most nested subquery). As noted, processing a query and/or subquery may include querying a data source with the query or subquery, respectively. Additionally, in some examples, the process 600 may include processing the continuous query based at least in part on the logical plan merger (e.g., from the nested subquery) to obtain a business event result at 606. Further, at 608, the process 600 may end by including receiving an exception when a dimension table changes. For example, a dimension table may define attributes or columns associated with the results of the subquery. When the underlying data (that is, the data that the continuous query will depend upon) changes, the exception may notify the query engine to refresh or reprocess the subquery.



FIG. 7 illustrates an example flow diagram showing process 700 for implementing the management of continuous queries in the presence of subqueries described herein. The one or more service provider computers 106 (e.g., utilizing at least the subquery module 148 of FIG. 1) shown in FIGS. 1-3 may perform the process 700 of FIG. 7. The process 700 may begin by including receiving a continuous query statement from a user associated with business event data at 702. At 704, the process 700 may include determining whether the continuous query includes a continuous subquery. In some cases, when it is determined that the continuous query does not include a subquery, the process 700 may end at 705 by including processing the continuous query to obtain results from a stream or relation. However, in other examples, it may be determined, at 704, that the continuous query does include a subquery. In this example, the process 700 may instead include processing the subquery to obtain a set of first results from a stream or relation at 706. At 708, the process 700 may also include processing the continuous query by utilizing the set of first results and/or the stream or relation data. Additionally, at 710, the process 700 may include skipping instantiation of an operator until data of a dimension table changes. At 712, the process 700 may include loading less than all of the set of first results into the continuous query (e.g., for data is not expected to change often). Further, the process 700 may end at 714, where the process 700 may include loading only a subset of the set of first results when data of the subset has a low probability of changing.



FIGS. 8-10 illustrate example flow diagrams showing respective processes 800, 900, and 1000 for implementing the mechanisms for chaining continuous queries described herein. These processes 800, 900, 1000 are illustrated as logical flow diagrams, each operation of which represents a sequence of operations that can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures and the like that perform particular functions or implement particular data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement the processes.


Additionally, some, any, or all of the processes may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable storage medium may be non-transitory.


In some examples, the one or more service provider computers 106 (e.g., utilizing at least the query chain module 149 of FIG. 1) shown in at least FIG. 1 may perform the process 800 of FIG. 8. The process 800 may begin by including storing results of a first continuous query in a data object at 802. The results may be the result of querying against a stream or relation with the first continuous query. At 804, the process 800 may include processing a second continuous query based at least in part on results stored in the data object. As such, the first and second continuous queries may be chained together by the data object. Additionally, at 806, the process 800 may include auditing the results of the data object. Alerts may be provided and/or, at 808, the process 800 may include mapping the results in the data object to a data visualization. At 810, the process 800 may also include preparing the visualization for display. Further, the process 800 may end at 812 by including enabling modification of the results in the data object.



FIG. 9 illustrates an example flow diagram showing process 900 for implementing the mechanisms for chaining continuous queries described herein. The one or more service provider computers 106 (e.g., utilizing at least one of the query chain module 149 of FIG. 1) shown in at least FIG. 1 may perform the process 900 of FIG. 9. The process 900 may begin at 902 by including initializing a first continuous query to collect a first result during a time interval. At 904, the process 900 may also include storing the first result in a data object (e.g., a Write Back DO configured to daisy chain continuous queries together). At 906, the process 900 may include submitting, based at least in part on a trigger, the first result of the data object to a second continuous query. Additionally, at 908, the process 900 may end by including providing an alert to a user based at least in part on the second result from the second continuous query. Further, multiple chains may be implemented by utilizing multiple different data objects configured to store results of the previous query for use by the next query.



FIG. 10 illustrates an example flow diagram showing process 1000 for implementing the mechanisms for chaining continuous queries described herein. The one or more service provider computers 106 (e.g., utilizing at least one of the query chain module 149 of FIG. 1) shown in at least FIG. 1 may perform the process 1000 of FIG. 10. The process 1000 may begin by including implementing at least a first continuous query on business event data of a user to collect a first result at 1002. At 1004, the process 1000 may also include storing the first result in a memory associated with a data object. The process 1000 may also include providing, based at least in part on a trigger, the first result stored in the data object to at least a second continuous query at 1006. At 1008, the process 1000 may also include providing, for display, a mapping of the first result and/or the second result to a business event visualization. Further, the process 100 may end, at 1010, by including providing an alert to the user based at least in part on the second result from the continuous query (e.g., falling outside a tolerance level or approaching a threshold).


Illustrative methods and systems for implementing the hybrid execution of continuous and scheduled queries are described above. Some or all of these systems and methods may, but need not, be implemented at least partially by architectures and processes such as those shown at least in FIGS. 1-10 above.



FIG. 11 is a simplified block diagram illustrating components of a system environment 1100 that may be used in accordance with an embodiment of the present disclosure. As shown, system environment 1100 includes one or more client computing devices 1102, 1104, 1106, 1108, which are configured to operate a client application such as a web browser, proprietary client (e.g., Oracle Forms), or the like over one or more networks 1110 (such as, but not limited to, networks similar to the networks 108 of FIGS. 1 and 3). In various embodiments, client computing devices 1102, 1104, 1106, and 1108 may interact with a server 1112 over the networks 1110.


Client computing devices 1102, 1104, 1106, 1108 may be general purpose personal computers (including, by way of example, personal computers and/or laptop computers running various versions of Microsoft Windows and/or Apple Macintosh operating systems), cell phones or PDAs (running software such as Microsoft Windows Mobile and being Internet, e-mail, SMS, Blackberry, or other communication protocol enabled), and/or workstation computers running any of a variety of commercially-available UNIX or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems). Alternatively, client computing devices 1102, 1104, 1106, and 1108 may be any other electronic device, such as a thin-client computer, Internet-enabled gaming system, and/or personal messaging device, capable of communicating over a network (e.g., network 1110 described below). Although exemplary system environment 1100 is shown with four client computing devices, any number of client computing devices may be supported. Other devices such as devices with sensors, etc. may interact with server 1112.


System environment 1100 may include networks 1110. Networks 1110 may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including without limitation TCP/IP, SNA, IPX, AppleTalk, and the like. Merely by way of example, network 1110 can be a local area network (LAN), such as an Ethernet network, a Token-Ring network and/or the like; a wide-area network; a virtual network, including without limitation a virtual private network (VPN); the Internet; an intranet; an extranet; a public switched telephone network (PSTN); an infra-red network; a wireless network (e.g., a network operating under any of the IEEE 802.11 suite of protocols, the Bluetooth protocol known in the art, and/or any other wireless protocol); and/or any combination of these and/or other networks.


System environment 1100 also includes one or more server computers 1112 which may be general purpose computers, specialized server computers (including, by way of example, PC servers, UNIX servers, mid-range servers, mainframe computers, rack-mounted servers, etc.), server farms, server clusters, or any other appropriate arrangement and/or combination. In various embodiments, server 1112 may be adapted to run one or more services or software applications described in the foregoing disclosure. For example, server 1112 may correspond to a server for performing processing described above according to an embodiment of the present disclosure.


Server 1112 may run an operating system including any of those discussed above, as well as any commercially available server operating system. Server 1112 may also run any of a variety of additional server applications and/or mid-tier applications, including HTTP servers, FTP servers, CGI servers, Java servers, database servers, and the like. Exemplary database servers include without limitation those commercially available from Oracle, Microsoft, Sybase, IBM and the like.


System environment 1100 may also include one or more databases 1114, 1116. Databases 1114, 1116 may reside in a variety of locations. By way of example, one or more of databases 1114, 1116 may reside on a non-transitory storage medium local to (and/or resident in) server 1112. Alternatively, databases 1114, 1116 may be remote from server 1112, and in communication with server 1112 via a network-based or dedicated connection. In one set of embodiments, databases 1114, 1116 may reside in a storage-area network (SAN) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to server 1112 may be stored locally on server 1112 and/or remotely, as appropriate. In one set of embodiments, databases 1114, 1116 may include relational databases, such as databases provided by Oracle, that are adapted to store, update, and retrieve data in response to SQL-formatted commands.



FIG. 12 is a simplified block diagram of a computer system 1200 that may be used in accordance with embodiments of the present disclosure. For example service provider computers 106 may be implemented using a system such as system 1200. Computer system 1200 is shown comprising hardware elements that may be electrically and/or communicatively coupled via a bus 1201. The hardware elements may include one or more central processing units (CPUs) 1202, one or more input devices 1204 (e.g., a mouse, a keyboard, etc.), and one or more output devices 1206 (e.g., a display device, a printer, etc.). Computer system 1200 may also include one or more storage devices 1208. By way of example, the storage device(s) 1208 may include devices such as disk drives, optical storage devices, and solid-state storage devices such as a random access memory (RAM) and/or a read-only memory (ROM), which can be programmable, flash-updateable and/or the like.


Computer system 1200 may additionally include a computer-readable storage media reader 1212, a communications subsystem 1214 (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.), and working memory 1218, which may include RAM and ROM devices as described above. In some embodiments, computer system 1200 may also include a processing acceleration unit 1216, which can include a digital signal processor (DSP), a special-purpose processor, and/or the like.


Computer-readable storage media reader 1212 can further be connected to a computer-readable storage medium 1210, together (and, optionally, in combination with storage device(s) 1208) comprehensively representing remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing computer-readable information. Communications system 1214 may permit data to be exchanged with network 1212 and/or any other computer described above with respect to system environment 1200.


Computer system 1200 may also comprise software elements, shown as being currently located within working memory 1218, including an operating system 1220 and/or other code 1222, such as an application program (which may be a client application, Web browser, mid-tier application, RDBMS, etc.). In an exemplary embodiment, working memory 1218 may include executable code and associated data structures used for relying party and open authorization-related processing as described above. It should be appreciated that alternative embodiments of computer system 1200 may have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input/output devices may be employed.


Storage media and computer readable media for containing code, or portions of code, can include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to, volatile and non-volatile (non-transitory), removable and non-removable media implemented in any method or technology for storage and/or transmission of information such as computer readable instructions, data structures, program modules, or other data, including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, data signals, data transmissions, or any other medium which can be used to store or transmit the desired information and which can be accessed by a computer.


Although specific embodiments of the disclosure have been described, various modifications, alterations, alternative constructions, and equivalents are also encompassed within the scope of the disclosure. Embodiments of the present disclosure are not restricted to operation within certain specific data processing environments, but are free to operate within a plurality of data processing environments. Additionally, although embodiments of the present disclosure have been described using a particular series of transactions and steps, it should be apparent to those skilled in the art that the scope of the present disclosure is not limited to the described series of transactions and steps.


Further, while embodiments of the present disclosure have been described using a particular combination of hardware and software, it should be recognized that other combinations of hardware and software are also within the scope of the present disclosure. Embodiments of the present disclosure may be implemented only in hardware, or only in software, or using combinations thereof.


The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that additions, subtractions, deletions, and other modifications and changes may be made thereunto without departing from the broader spirit and scope. Illustrative methods and systems for providing features of the present disclosure are described above. Some or all of these systems and methods may, but need not, be implemented at least partially by architectures such as those shown in FIGS. 1-12 above.


Although embodiments have been described in language specific to structural features and/or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments do not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements, and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and/or steps are included or are to be performed in any particular embodiment.

Claims
  • 1. A system, comprising: a memory storing a plurality of instructions; andone or more processors configured to access the memory, wherein the one or more processors are further configured to execute the plurality of instructions to at least: identify a continuous query;determine whether the continuous query includes one or more continuous subqueries; andwhen it is determined that the continuous query includes a continuous subquery of the one or more continuous subqueries: process the continuous subquery to obtain first results from a time-varying relation associated with a data stream, the time-varying relation comprising a bounded window on the data stream and a same schema for each event of the data stream;store only a subset of the first results from the continuous subquery in a dimension table when the subset of the first results is associated with a low probability of change;issue the continuous query based at least in part on the dimension table corresponding to the continuous subquery;receive a runtime exception;determine whether the runtime exception is a known runtime exception that is understood by a service based at least in part on a format of the runtime exception;when the format of the runtime exception is the known runtime exception: identify that a change to the dimension table has occurred;refresh the dimension table by reprocessing the continuous subquery based at least in part on the runtime exception; andreissue the continuous query to obtain second results after the dimension table is refreshed;store the second results of the reissued continuous query in a data object;implement a pattern match query on the second results stored in the data object;perform trend analysis on the pattern match query results;provide, for display by a user interface, a mapping of the trend analysis to a visualization;identify a pattern corresponding to a performance indicator from the trend analysis; andprovide an alert in the user interface based at least in part on the pattern identified from the trend analysis.
  • 2. The system of claim 1, wherein the time-varying relation comprises an unordered, time-varying set of tuples associated with the stream of business event data.
  • 3. The system of claim 1, wherein identifying the continuous query includes at least receiving the continuous query or generating the continuous query.
  • 4. The system of claim 1, wherein the user interface is provided to a user that provided the continuous query.
  • 5. The system of claim 4, wherein the user interface is configured to display real-time data based at least in part on the second results.
  • 6. The system of claim 1, wherein the continuous query is dependent on the dimension table from the continuous subquery.
  • 7. The system of claim 1, wherein the continuous subquery is included within a “from” clause or a “set” clause of the continuous query.
  • 8. The system of claim 1, wherein the continuous subquery is configured to obtain the results over time, and wherein less than all of the results are stored in the dimension table and accessible for processing the continuous query to obtain the second result.
  • 9. A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising: instructions that cause the one or more processors to receive a continuous query statement from a user associated with business event data;instructions that cause the one or more processors to determine whether the continuous query statement includes one or more nested subquery statements; andinstructions that cause the one or more processors to, when it is determined that the continuous query statement includes a nested subquery statement of the one or more nested sub query statements: process the nested subquery statement to obtain first results corresponding to a time-varying relation associated with the business event data, the time-varying relation comprising a bounded window on the a data stream of the business event data and a same schema for each event of the data stream of business event data;store only a subset of the first results from the nested subquery statement in a dimension table when the subset of the first results is associated with a low probability of change;issue the continuous query based at least in part on the dimension table corresponding to the nested subquery statement;receive a runtime exception;determine whether the runtime exception is a known runtime exception that is understood by a service based at least in part on a format of the runtime exception;when the format of the runtime extension is the known runtime exception: identify that a change to the dimension table has occurred;refresh the dimension table by reprocessing the nested subquery statement based at least in part on the runtime exception; andreissue the continuous query statement to obtain second results after the dimension table is refreshed;store the second results of the reissued continuous query in a data object;implement a pattern match query on the second results stored in the data object;perform trend analysis on the pattern match query results;provide, for display by a user interface, a mapping of the trend analysis to a visualization;identify a pattern corresponding to a performance indicator from the trend analysis; andprovide an alert in the user interface based at least in part on the pattern identified from the trend analysis.
  • 10. The non-transitory computer-readable memory of claim 9, wherein the nested subquery statement includes at least another subquery statement.
  • 11. A computer-implemented method, comprising: receiving a continuous query statement from a user associated with business event data;determining whether the continuous query includes one or more continuous subqueries; andwhen the continuous query includes a continuous subquery of the one or more continuous subqueries: processing the continuous subquery to obtain a set of first results based at least in part on implementing a clause of the continuous subquery on a time-varying relation associated with a stream associated with the business event data of the user, the time-varying relation comprising a bounded window on the stream and a same schema for each event of the data stream;storing only a subset of the set of first results from the continuous subquery in a dimension table when the subset of the first results is associated with a low probability of change;issue the continuous query by utilizing at least a subset of the dimension table based at least in part on implementing a clause of the continuous query on the stream associated with the business event data of the user;receiving a runtime exception;determining whether the runtime exception is a known runtime exception that is understood by a service based at least in part on a format of the runtime exception;when the format of the runtime exception is the known runtime exception: identifying that a change to the dimension table has occurred;refreshing the dimension table by reprocessing the continuous subquery based at least in part on the runtime exception; andreissuing the continuous query to obtain second results after the dimension table is refreshed;storing the second results of the reissued continuous query in a data object;implement a pattern match query on the second results stored in the data object;perform trend analysis on the pattern match query results;provide, for display by a user interface, a mapping of the trend analysis to a visualization;identify a pattern corresponding to a performance indicator from the trend analysis; andprovide an alert in the user interface based at least in part on the pattern identified from the trend analysis.
  • 12. The computer-implemented method of 11, further comprising not instantiating an operator of the continuous query until a time after the data in the dimension table has changed.
  • 13. The computer-implemented method of 11, further comprising loading less than all of the set of first results in the dimension table.
  • 14. The computer-implemented method of claim 11, wherein the time-varying relation comprises an archived relation.
CROSS REFERENCES TO RELATED APPLICATIONS

The present application is a non-provisional of and claims the benefit and priority under 35 U.S.C. 119(e) of U.S. Provisional Application No. 61/707,641, filed Sep. 28, 2012, entitled “REAL-TIME BUSINESS EVENT ANALYSIS AND MONITORING”, the entire contents of which are incorporated herein by reference for all purposes. This application is also related to U.S. application Ser. No. 13/830,735, filed on Mar. 14, 2013, entitled “MECHANISM TO CHAIN CONTINUOUS QUERIES,” now U.S. Pat. No. 9,946,756, the entire contents of which is hereby incorporated by reference as if fully set forth herein, under 35 U.S.C. § 120.

US Referenced Citations (550)
Number Name Date Kind
4996687 Hess et al. Feb 1991 A
5051947 Messenger et al. Sep 1991 A
5339392 Risberg et al. Aug 1994 A
5495600 Terry et al. Feb 1996 A
5691917 Harrison Nov 1997 A
5706494 Cochrane et al. Jan 1998 A
5802262 Van De Sep 1998 A
5802523 Jasuja et al. Sep 1998 A
5822750 Jou et al. Oct 1998 A
5826077 Blakeley et al. Oct 1998 A
5850544 Parvathaneny et al. Dec 1998 A
5857182 DeMichiel et al. Jan 1999 A
5918225 White et al. Jun 1999 A
5920716 Johnson et al. Jul 1999 A
5937195 Ju et al. Aug 1999 A
5937401 Hillegas et al. Aug 1999 A
6006235 Macdonald et al. Dec 1999 A
6011916 Moore et al. Jan 2000 A
6041344 Bodamer et al. Mar 2000 A
6081801 Cochrane et al. Jun 2000 A
6092065 Floratos et al. Jul 2000 A
6108666 Floratos et al. Aug 2000 A
6112198 Lohman et al. Aug 2000 A
6128610 Srinivasan et al. Oct 2000 A
6158045 You Dec 2000 A
6212673 House et al. Apr 2001 B1
6219660 Haderle et al. Apr 2001 B1
6263332 Nasr et al. Jul 2001 B1
6278994 Fuh et al. Aug 2001 B1
6282537 Madnick et al. Aug 2001 B1
6341281 MacNicol et al. Jan 2002 B1
6353821 Gray et al. Mar 2002 B1
6367034 Novik et al. Apr 2002 B1
6370537 Gilbert et al. Apr 2002 B1
6389436 Chakrabarti et al. May 2002 B1
6397262 Hayden et al. May 2002 B1
6418448 Sakar Jul 2002 B1
6438540 Nasr et al. Aug 2002 B2
6438559 White et al. Aug 2002 B1
6439783 Antoshenkov Aug 2002 B1
6449620 Draper et al. Sep 2002 B1
6453314 Chan et al. Sep 2002 B1
6507834 Kabra et al. Jan 2003 B1
6523102 Dye et al. Feb 2003 B1
6546381 Subramanian et al. Apr 2003 B1
6615203 Lin et al. Sep 2003 B1
6633867 Kraft et al. Oct 2003 B1
6681343 Nakabo Jan 2004 B1
6708186 Claborn et al. Mar 2004 B1
6718278 Steggles Apr 2004 B1
6748386 Li Jun 2004 B1
6751619 Rowstron et al. Jun 2004 B1
6766330 Chen et al. Jul 2004 B1
6785677 Fritchman Aug 2004 B1
6826566 Lewak et al. Nov 2004 B2
6836778 Manikutty et al. Dec 2004 B2
6850925 Chaudhuri et al. Feb 2005 B2
6856981 Wyschogrod et al. Feb 2005 B2
6904019 Heinen et al. Jun 2005 B2
6985904 Kaluskar et al. Jan 2006 B1
6986019 Bagashev et al. Jan 2006 B1
6996557 Leung et al. Feb 2006 B1
7020696 Perry et al. Mar 2006 B1
7047249 Vincent May 2006 B1
7051034 Ghosh et al. May 2006 B1
7062749 Cyr et al. Jun 2006 B2
7080062 Leung et al. Jul 2006 B1
7093023 Lockwood et al. Aug 2006 B2
7145938 Takeuchi et al. Dec 2006 B2
7146352 Brundage et al. Dec 2006 B2
7167848 Boukouvalas et al. Jan 2007 B2
7203927 Al-Azzawe et al. Apr 2007 B2
7224185 Campbell et al. May 2007 B2
7225188 Gai et al. May 2007 B1
7236972 Lewak et al. Jun 2007 B2
7284041 Nakatani et al. Oct 2007 B2
7305391 Wyschogrod et al. Dec 2007 B2
7308561 Cornet et al. Dec 2007 B2
7310638 Blair Dec 2007 B1
7348981 Buck Mar 2008 B1
7376656 Blakeley et al. May 2008 B2
7383253 Tsimelzon et al. Jun 2008 B1
7403959 Nishizawa et al. Jul 2008 B2
7430549 Zane et al. Sep 2008 B2
7440461 Sahita et al. Oct 2008 B2
7451143 Sharangpani et al. Nov 2008 B2
7475058 Kakivaya et al. Jan 2009 B2
7483976 Ross Jan 2009 B2
7516121 Liu et al. Apr 2009 B2
7519577 Brundage et al. Apr 2009 B2
7519962 Aman Apr 2009 B2
7526804 Shelest et al. Apr 2009 B2
7533087 Liu et al. May 2009 B2
7546284 Martinez et al. Jun 2009 B1
7552365 Marsh et al. Jun 2009 B1
7567953 Kadayam et al. Jul 2009 B2
7580946 Mansour et al. Aug 2009 B2
7587383 Koo et al. Sep 2009 B2
7603674 Cyr et al. Oct 2009 B2
7613848 Amini et al. Nov 2009 B2
7620851 Leavy et al. Nov 2009 B1
7630982 Boyce et al. Dec 2009 B2
7634501 Yabloko Dec 2009 B2
7636703 Taylor et al. Dec 2009 B2
7644066 Krishnaprasad et al. Jan 2010 B2
7653645 Stokes Jan 2010 B1
7672964 Yan et al. Mar 2010 B1
7673065 Srinivasan et al. Mar 2010 B2
7676461 Chkodrov et al. Mar 2010 B2
7689622 Liu et al. Mar 2010 B2
7693891 Stokes et al. Apr 2010 B2
7702629 Cytron et al. Apr 2010 B2
7702639 Stanley et al. Apr 2010 B2
7711782 Kim et al. May 2010 B2
7716210 Ozcan et al. May 2010 B2
7739265 Jain et al. Jun 2010 B2
7805445 Boyer et al. Sep 2010 B2
7814111 Levin Oct 2010 B2
7818313 Tsimelzon et al. Oct 2010 B1
7823066 Kuramura Oct 2010 B1
7827146 De Landstheer et al. Nov 2010 B1
7827190 Pandya Nov 2010 B2
7844829 Meenakshisundaram Nov 2010 B2
7870124 Liu et al. Jan 2011 B2
7870167 Lu et al. Jan 2011 B2
7877381 Ewen et al. Jan 2011 B2
7895187 Bowman Feb 2011 B2
7912853 Agrawal Mar 2011 B2
7917299 Buhler et al. Mar 2011 B2
7930322 Maclennan Apr 2011 B2
7945540 Park et al. May 2011 B2
7953728 Hu et al. May 2011 B2
7954109 Durham et al. May 2011 B1
7979420 Jain et al. Jul 2011 B2
7984043 Waas Jul 2011 B1
7987204 Stokes Jul 2011 B2
7988817 Son Aug 2011 B2
7991766 Srinivasan et al. Aug 2011 B2
7996388 Jain et al. Aug 2011 B2
8019747 Srinivasan et al. Sep 2011 B2
8032544 Jing et al. Oct 2011 B2
8046747 Cyr et al. Oct 2011 B2
8073826 Srinivasan et al. Dec 2011 B2
8099400 Haub et al. Jan 2012 B2
8103655 Srinivasan et al. Jan 2012 B2
8122006 De Castro et al. Feb 2012 B2
8134184 Becker et al. Mar 2012 B2
8145686 Raman et al. Mar 2012 B2
8145859 Park et al. Mar 2012 B2
8155880 Patel et al. Apr 2012 B2
8190738 Ruehle May 2012 B2
8195648 Zabback et al. Jun 2012 B2
8204873 Chavan Jun 2012 B2
8204875 Srinivasan et al. Jun 2012 B2
8260803 Hsu et al. Sep 2012 B2
8290776 Moriwaki et al. Oct 2012 B2
8296316 Jain et al. Oct 2012 B2
8307197 Koch, III Nov 2012 B2
8307343 Chaudhuri et al. Nov 2012 B2
8315990 Barga et al. Nov 2012 B2
8316012 Abouzied et al. Nov 2012 B2
8321450 Thatte et al. Nov 2012 B2
8332502 Neuhaus et al. Dec 2012 B1
8346511 Schoning et al. Jan 2013 B2
8352517 Park et al. Jan 2013 B2
8370812 Feblowitz et al. Feb 2013 B2
8386466 Park et al. Feb 2013 B2
8387076 Thatte et al. Feb 2013 B2
8392402 Mihaila et al. Mar 2013 B2
8396886 Tsimelzon Mar 2013 B1
8447744 De Castro Alves et al. May 2013 B2
8458175 Stokes Jun 2013 B2
8498956 Srinivasan et al. Jul 2013 B2
8521867 Srinivasan et al. Aug 2013 B2
8527458 Park et al. Sep 2013 B2
8543558 Srinivasan et al. Sep 2013 B2
8572589 Cataldo et al. Oct 2013 B2
8589436 Srinivasan et al. Nov 2013 B2
8595840 Malibiran et al. Nov 2013 B1
8676841 Srinivasan et al. Mar 2014 B2
8713038 Cohen et al. Apr 2014 B2
8713049 Jain et al. Apr 2014 B2
8719207 Ratnam et al. May 2014 B2
8738572 Bird et al. May 2014 B2
8745070 Krisnamurthy Jun 2014 B2
8762369 Macho et al. Jun 2014 B2
8775412 Day et al. Jul 2014 B2
8880493 Chen et al. Nov 2014 B2
9015102 van Lunteren Apr 2015 B2
9058360 De Castro Alves et al. Jun 2015 B2
9098587 Deshmukh et al. Aug 2015 B2
9110945 Jain Aug 2015 B2
9189280 Park et al. Nov 2015 B2
9244978 Alves et al. Jan 2016 B2
9256646 Deshmukh et al. Feb 2016 B2
9262258 Alves et al. Feb 2016 B2
9262479 Deshmukh et al. Feb 2016 B2
9286352 Park et al. Mar 2016 B2
9292574 Hsiao et al. Mar 2016 B2
9305057 De Castro Alves et al. Apr 2016 B2
9305238 Srinivasan et al. Apr 2016 B2
9329975 Park et al. May 2016 B2
9361308 Deshmukh et al. Jun 2016 B2
9390135 Alves et al. Jul 2016 B2
9418113 Bishnoi et al. Aug 2016 B2
9430494 Park et al. Aug 2016 B2
9535761 Park et al. Jan 2017 B2
9563663 Shukla et al. Feb 2017 B2
9703836 Hsiao et al. Jul 2017 B2
9712645 de Castro Alves et al. Jul 2017 B2
9715529 Park et al. Jul 2017 B2
9756104 Shukla et al. Sep 2017 B2
9804892 Park et al. Oct 2017 B2
9805095 Deshmukh et al. Oct 2017 B2
9852186 Herwadkar et al. Dec 2017 B2
9886486 De Castro Alves et al. Feb 2018 B2
20020023211 Roth et al. Feb 2002 A1
20020032804 Hunt Mar 2002 A1
20020038306 Griffin Mar 2002 A1
20020038313 Klein et al. Mar 2002 A1
20020049788 Lipkin Apr 2002 A1
20020056004 Smith et al. May 2002 A1
20020073399 Golden Jun 2002 A1
20020116362 Li et al. Aug 2002 A1
20020116371 Dodds et al. Aug 2002 A1
20020133484 Chau et al. Sep 2002 A1
20020169788 Lee et al. Nov 2002 A1
20030014408 Robertson Jan 2003 A1
20030037048 Kabra et al. Feb 2003 A1
20030046673 Copeland et al. Mar 2003 A1
20030065655 Syeda-mahmood Apr 2003 A1
20030065659 Agarwal et al. Apr 2003 A1
20030120682 Bestgen et al. Jun 2003 A1
20030135304 Sroub et al. Jul 2003 A1
20030200198 Chandrasekar et al. Oct 2003 A1
20030212664 Breining et al. Nov 2003 A1
20030229652 Bakalash et al. Dec 2003 A1
20030236766 Fortuna et al. Dec 2003 A1
20040010496 Behrendt et al. Jan 2004 A1
20040019592 Crabtree Jan 2004 A1
20040024773 Stoffel et al. Feb 2004 A1
20040064466 Manikutty et al. Apr 2004 A1
20040073534 Robson Apr 2004 A1
20040088404 Aggarwal May 2004 A1
20040117359 Snodgrass et al. Jun 2004 A1
20040136598 Le Leannec et al. Jul 2004 A1
20040151382 Stellenberg et al. Aug 2004 A1
20040153329 Casati et al. Aug 2004 A1
20040167864 Wang et al. Aug 2004 A1
20040168107 Sharp et al. Aug 2004 A1
20040177053 Donoho et al. Sep 2004 A1
20040201612 Hild et al. Oct 2004 A1
20040205082 Fontoura et al. Oct 2004 A1
20040220896 Finlay et al. Nov 2004 A1
20040220912 Manikutty et al. Nov 2004 A1
20040220927 Murthy et al. Nov 2004 A1
20040243590 Gu et al. Dec 2004 A1
20040267760 Brundage et al. Dec 2004 A1
20040268314 Kollman et al. Dec 2004 A1
20050010896 Meliksetian et al. Jan 2005 A1
20050027698 Collet et al. Feb 2005 A1
20050055338 Warner et al. Mar 2005 A1
20050065949 Warner et al. Mar 2005 A1
20050096124 Stronach May 2005 A1
20050097128 Ryan et al. May 2005 A1
20050108368 Mohan May 2005 A1
20050120016 Midgley Jun 2005 A1
20050154740 Day et al. Jul 2005 A1
20050174940 Iny Aug 2005 A1
20050177579 Blakeley et al. Aug 2005 A1
20050192921 Chaudhuri et al. Sep 2005 A1
20050204340 Ruminer et al. Sep 2005 A1
20050229158 Thusoo et al. Oct 2005 A1
20050273352 Moffat et al. Dec 2005 A1
20050273450 McMillen et al. Dec 2005 A1
20050289125 Liu et al. Dec 2005 A1
20060007308 Ide et al. Jan 2006 A1
20060015482 Beyer et al. Jan 2006 A1
20060031204 Liu et al. Feb 2006 A1
20060047696 Larson et al. Mar 2006 A1
20060064487 Ross Mar 2006 A1
20060080646 Aman Apr 2006 A1
20060085592 Ganguly et al. Apr 2006 A1
20060089939 Broda et al. Apr 2006 A1
20060100957 Buttler et al. May 2006 A1
20060100969 Wang et al. May 2006 A1
20060106786 Day et al. May 2006 A1
20060106797 Srinivasa et al. May 2006 A1
20060129554 Suyama et al. Jun 2006 A1
20060155719 Mihaeli et al. Jul 2006 A1
20060166704 Benco et al. Jul 2006 A1
20060167704 Nicholls et al. Jul 2006 A1
20060167856 Angele et al. Jul 2006 A1
20060167869 Jones Jul 2006 A1
20060212441 Tang et al. Sep 2006 A1
20060224576 Liu et al. Oct 2006 A1
20060230029 Yan Oct 2006 A1
20060235840 Manikutty et al. Oct 2006 A1
20060242180 Graf et al. Oct 2006 A1
20060282429 Hernandez-Sherrington et al. Dec 2006 A1
20060294095 Berk et al. Dec 2006 A1
20070016467 John et al. Jan 2007 A1
20070039049 Kupferman et al. Feb 2007 A1
20070050340 Von Kaenel et al. Mar 2007 A1
20070076314 Rigney Apr 2007 A1
20070118600 Arora May 2007 A1
20070136239 Lee et al. Jun 2007 A1
20070136254 Choi et al. Jun 2007 A1
20070156787 MacGregor Jul 2007 A1
20070156964 Sistla Jul 2007 A1
20070168154 Ericson Jul 2007 A1
20070192301 Posner Aug 2007 A1
20070198479 Cai et al. Aug 2007 A1
20070022092 Nishizawa et al. Sep 2007 A1
20070214171 Behnen et al. Sep 2007 A1
20070226188 Johnson et al. Sep 2007 A1
20070226239 Johnson et al. Sep 2007 A1
20070250487 Reuther Oct 2007 A1
20070271280 Chandasekaran Nov 2007 A1
20070294217 Chen et al. Dec 2007 A1
20080005093 Liu et al. Jan 2008 A1
20080010093 LaPlante et al. Jan 2008 A1
20080010241 Mcgoveran Jan 2008 A1
20080016095 Bhatnagar et al. Jan 2008 A1
20080028095 Lang et al. Jan 2008 A1
20080033914 Cherniack et al. Feb 2008 A1
20080034427 Cadambi et al. Feb 2008 A1
20080046401 Lee et al. Feb 2008 A1
20080071904 Schuba et al. Mar 2008 A1
20080077570 Tang et al. Mar 2008 A1
20080077587 Wyschogrod et al. Mar 2008 A1
20080077780 Zingher Mar 2008 A1
20080082484 Averbuch et al. Apr 2008 A1
20080082514 Khorlin et al. Apr 2008 A1
20080086321 Walton Apr 2008 A1
20080098359 Ivanov et al. Apr 2008 A1
20080098370 Fontoura et al. Apr 2008 A1
20080110397 Son May 2008 A1
20080114787 Kashiyama et al. May 2008 A1
20080120283 Liu et al. May 2008 A1
20080120321 Liu et al. May 2008 A1
20080162583 Brown et al. Jul 2008 A1
20080195577 Fan et al. Aug 2008 A1
20080235298 Lin et al. Sep 2008 A1
20080243451 Feblowitz et al. Oct 2008 A1
20080243675 Parsons et al. Oct 2008 A1
20080250073 Nori et al. Oct 2008 A1
20080255847 Moriwaki et al. Oct 2008 A1
20080263039 Van Lunteren Oct 2008 A1
20080270764 McMillen et al. Oct 2008 A1
20080275891 Park et al. Nov 2008 A1
20080281782 Agrawal Nov 2008 A1
20080301086 Gupta Dec 2008 A1
20080301124 Alves et al. Dec 2008 A1
20080301125 Alves et al. Dec 2008 A1
20080301135 Alves et al. Dec 2008 A1
20080301256 Mcwilliams et al. Dec 2008 A1
20080313131 Friedman et al. Dec 2008 A1
20090006320 Ding et al. Jan 2009 A1
20090006346 Kanthi et al. Jan 2009 A1
20090007098 Chevrette et al. Jan 2009 A1
20090019045 Amir et al. Jan 2009 A1
20090024622 Chkodrov et al. Jan 2009 A1
20090043729 Liu et al. Feb 2009 A1
20090070355 Cadarette et al. Mar 2009 A1
20090070785 Alvez et al. Mar 2009 A1
20090070786 Alves Mar 2009 A1
20090076899 Gbodimowo Mar 2009 A1
20090088962 Jones Apr 2009 A1
20090100029 Jain et al. Apr 2009 A1
20090106189 Jain et al. Apr 2009 A1
20090106190 Srinivasan et al. Apr 2009 A1
20090106198 Srinivasan et al. Apr 2009 A1
20090106214 Jain et al. Apr 2009 A1
20090106215 Jain et al. Apr 2009 A1
20090106218 Srinivasan et al. Apr 2009 A1
20090106321 Das et al. Apr 2009 A1
20090106440 Srinivasan et al. Apr 2009 A1
20090112779 Wolf et al. Apr 2009 A1
20090112802 Srinivasan et al. Apr 2009 A1
20090112803 Srinivasan et al. Apr 2009 A1
20090112853 Nishizawa et al. Apr 2009 A1
20090125550 Barga et al. May 2009 A1
20090125916 Lu et al. May 2009 A1
20090132503 Sun et al. May 2009 A1
20090133041 Rahman et al. May 2009 A1
20090144696 Andersen Jun 2009 A1
20090172014 Huetter Jul 2009 A1
20090182779 Johnson Jul 2009 A1
20090187584 Johnson et al. Jul 2009 A1
20090192981 Papaemmanouil et al. Jul 2009 A1
20090216747 Li et al. Aug 2009 A1
20090216860 Li et al. Aug 2009 A1
20090222730 Wixson et al. Sep 2009 A1
20090228431 Dunagan et al. Sep 2009 A1
20090228434 Krishnamurthy et al. Sep 2009 A1
20090228465 Krishnamurthy et al. Sep 2009 A1
20090245236 Scott et al. Oct 2009 A1
20090248749 Gu et al. Oct 2009 A1
20090254522 Chaudhuri et al. Oct 2009 A1
20090257314 Davis et al. Oct 2009 A1
20090265324 Mordvinov et al. Oct 2009 A1
20090271529 Kashiyama et al. Oct 2009 A1
20090282021 Bennet et al. Nov 2009 A1
20090292979 Aggarwal Nov 2009 A1
20090293046 Cheriton Nov 2009 A1
20090300093 Griffiths et al. Dec 2009 A1
20090300181 Marques Dec 2009 A1
20090300580 Heyhoe et al. Dec 2009 A1
20090300615 Andrade et al. Dec 2009 A1
20090313198 Kudo et al. Dec 2009 A1
20090319501 Goldstein et al. Dec 2009 A1
20090327102 Maniar et al. Dec 2009 A1
20090327257 Abouzeid et al. Dec 2009 A1
20100017379 Naibo et al. Jan 2010 A1
20100017380 Naibo et al. Jan 2010 A1
20100022627 Scherer et al. Jan 2010 A1
20100023498 Dettinger et al. Jan 2010 A1
20100036803 Vemuri et al. Feb 2010 A1
20100036831 Vemuri et al. Feb 2010 A1
20100049710 Young, Jr. et al. Feb 2010 A1
20100057663 Srinivasan et al. Mar 2010 A1
20100057727 Srinivasan et al. Mar 2010 A1
20100057735 Srinivasan et al. Mar 2010 A1
20100057736 Srinivasan et al. Mar 2010 A1
20100057737 Srinivasan et al. Mar 2010 A1
20100094838 Kozak Apr 2010 A1
20100106710 Nishikawa Apr 2010 A1
20100106946 Imaki et al. Apr 2010 A1
20100125572 Poblete et al. May 2010 A1
20100125574 Navas May 2010 A1
20100125584 Navas May 2010 A1
20100138405 Mihaila et al. Jun 2010 A1
20100161589 Nica et al. Jun 2010 A1
20100223283 Lee et al. Sep 2010 A1
20100223305 Park et al. Sep 2010 A1
20100223437 Park et al. Sep 2010 A1
20100223606 Park et al. Sep 2010 A1
20100250572 Chen Sep 2010 A1
20100293135 Candea et al. Nov 2010 A1
20100312756 Zhang et al. Dec 2010 A1
20100318652 Samba Dec 2010 A1
20100332401 Prahlad et al. Dec 2010 A1
20110004621 Kelley et al. Jan 2011 A1
20110016123 Pandey et al. Jan 2011 A1
20110016160 Zhang et al. Jan 2011 A1
20110022618 Thatte et al. Jan 2011 A1
20110023055 Thatte et al. Jan 2011 A1
20110029484 Park et al. Feb 2011 A1
20110029485 Park et al. Feb 2011 A1
20110035253 Mason et al. Feb 2011 A1
20110040746 Handa et al. Feb 2011 A1
20110040827 Katsunuma et al. Feb 2011 A1
20110055192 Tang et al. Mar 2011 A1
20110055197 Chavan Mar 2011 A1
20110084967 De Pauw et al. Apr 2011 A1
20110093162 Nielsen et al. Apr 2011 A1
20110105857 Zhang et al. May 2011 A1
20110131588 Allam et al. Jun 2011 A1
20110161321 De Castro et al. Jun 2011 A1
20110161328 Park et al. Jun 2011 A1
20110161352 De Castro et al. Jun 2011 A1
20110161356 De Castro et al. Jun 2011 A1
20110161397 Bekiares et al. Jun 2011 A1
20110173231 Drissi et al. Jul 2011 A1
20110173235 Aman et al. Jul 2011 A1
20110196839 Smith et al. Aug 2011 A1
20110196891 De Castro et al. Aug 2011 A1
20110213802 Singh et al. Sep 2011 A1
20110246445 Mishra Oct 2011 A1
20110270879 Srinivasan et al. Nov 2011 A1
20110282812 Chandramouli et al. Nov 2011 A1
20110295841 Sityon et al. Dec 2011 A1
20110302164 Krishnamurthy et al. Dec 2011 A1
20110313844 Chandramouli et al. Dec 2011 A1
20110314019 Jimenez Peris et al. Dec 2011 A1
20110321057 Mejdrich et al. Dec 2011 A1
20120016866 Dunagan Jan 2012 A1
20120041934 Srinivasan et al. Feb 2012 A1
20120072455 Jain et al. Mar 2012 A1
20120116982 Yoshida et al. May 2012 A1
20120130963 Luo et al. May 2012 A1
20120131139 Siripurapu et al. May 2012 A1
20120166417 Chandramouli et al. Jun 2012 A1
20120166421 Cammert et al. Jun 2012 A1
20120166469 Cammert et al. Jun 2012 A1
20120191697 Sherman et al. Jul 2012 A1
20120233107 Roesch et al. Sep 2012 A1
20120259910 Andrade et al. Oct 2012 A1
20120278473 Griffiths Nov 2012 A1
20120284420 Shukla et al. Nov 2012 A1
20120290715 Dinger et al. Nov 2012 A1
20120291049 Park et al. Nov 2012 A1
20120324453 Chandramouli et al. Dec 2012 A1
20130014088 Park et al. Jan 2013 A1
20130031567 Nano et al. Jan 2013 A1
20130046725 Cammert et al. Feb 2013 A1
20130117317 Wolf May 2013 A1
20130144866 Jerzak et al. Jun 2013 A1
20130191370 Chen et al. Jul 2013 A1
20130262399 Eker et al. Oct 2013 A1
20130275452 Krishnamurthy et al. Oct 2013 A1
20130332240 Patri et al. Dec 2013 A1
20140019194 Anne et al. Jan 2014 A1
20140059109 Jugel et al. Feb 2014 A1
20140082013 Wolf et al. Mar 2014 A1
20140095425 Sipple et al. Apr 2014 A1
20140095444 Deshmukh et al. Apr 2014 A1
20140095445 Deshmukh et al. Apr 2014 A1
20140095446 Deshmukh et al. Apr 2014 A1
20140095447 Deshmukh et al. Apr 2014 A1
20140095462 Park et al. Apr 2014 A1
20140095471 Deshmukh et al. Apr 2014 A1
20140095483 Toillion et al. Apr 2014 A1
20140095525 Hsiao et al. Apr 2014 A1
20140095529 Deshmukh et al. Apr 2014 A1
20140095533 Shukla et al. Apr 2014 A1
20140095535 Deshmukh et al. Apr 2014 A1
20140095537 Park et al. Apr 2014 A1
20140095540 Hsiao et al. Apr 2014 A1
20140095541 Herwadkar et al. Apr 2014 A1
20140095543 Hsiao et al. Apr 2014 A1
20140136514 Jain et al. May 2014 A1
20140156683 de Castro Alves Jun 2014 A1
20140172506 Parsell et al. Jun 2014 A1
20140172914 Elnikety et al. Jun 2014 A1
20140201225 Deshmukh et al. Jul 2014 A1
20140201355 Bishnoi et al. Jul 2014 A1
20140236983 Alves et al. Aug 2014 A1
20140237289 de Castro Alves et al. Aug 2014 A1
20140237487 Prasanna et al. Aug 2014 A1
20140324530 Thompson et al. Oct 2014 A1
20140358959 Bishnoi et al. Dec 2014 A1
20140379712 Lafuente Alvarez Dec 2014 A1
20150007320 Liu et al. Jan 2015 A1
20150156241 Shukla et al. Jun 2015 A1
20150161214 Kali et al. Jun 2015 A1
20150227415 Alves et al. Aug 2015 A1
20150363464 Alves et al. Dec 2015 A1
20150381712 De Castro Alves et al. Dec 2015 A1
20160034311 Park et al. Feb 2016 A1
20160085809 De Castro et al. Mar 2016 A1
20160085810 De Castro et al. Mar 2016 A1
20160103882 Deshmukh et al. Apr 2016 A1
20160127517 Shcherbakov et al. May 2016 A1
20160140180 Park et al. May 2016 A1
20160154855 Hsiao et al. Jun 2016 A1
20160283555 Alves et al. Sep 2016 A1
20170024912 De Castro et al. Jan 2017 A1
20170075726 Park et al. Mar 2017 A1
Foreign Referenced Citations (38)
Number Date Country
101866353 Oct 2010 CN
102135984 Jul 2011 CN
102665207 Sep 2012 CN
102892073 Jan 2013 CN
104885077 Sep 2015 CN
104937591 Sep 2015 CN
105593854 May 2016 CN
1 241 589 Sep 2002 EP
2474922 Jul 2012 EP
2946314 Nov 2015 EP
2946527 Nov 2015 EP
2959408 Dec 2015 EP
2002-251233 Sep 2002 JP
2006338432 Dec 2006 JP
2007-328716 Dec 2007 JP
2008-541225 Nov 2008 JP
2009-134689 Jun 2009 JP
2009171193 Jul 2009 JP
2010-108073 May 2010 JP
2011-038818 Feb 2011 JP
2015536001 Dec 2015 JP
2016500167 Jan 2016 JP
0049533 Aug 2000 WO
0118712 Mar 2001 WO
1059602 Aug 2001 WO
0165418 Sep 2001 WO
03030031 Apr 2003 WO
2007122347 Nov 2007 WO
WO2009119811 Oct 2009 WO
2010050582 May 2010 WO
2012037511 Mar 2012 WO
2012050582 Apr 2012 WO
WO-2012050582 Apr 2012 WO
2012154408 Nov 2012 WO
2012158360 Nov 2012 WO
2014000819 Jan 2014 WO
2015191120 Dec 2015 WO
2016048912 Mar 2016 WO
Non-Patent Literature Citations (519)
Entry
Arasu et al., “The CQL continuous query language: semantic foundations and query execution” Jul. 22, 2005, The VLDB Journal vol. 15 Issue 2, [retrieved on Nov. 13, 2014], retrieved from the internet <URL:http://dl.acm.org/citation.cfm?id=1146463>.
Babu et al., “Exploiting k-Constraints to Reduce Memory Overhead in Continuous Queries Over Data Streams”, ACM Transactions on Database Systems (TODS) vol. 29 Issue 3, Sep. 2004, [retrieved on Nov. 13, 2014], retrieved from the internet <URL:http://dl.acm.org/citation.cfm?id=1016032>.
Chen et al., “NiagaraCQ: A Scaleable Continuous Query Sytem for Internet Databases”, Newsletter: ACM SIGMOD Record vol. 29 Issue 2, Jun. 2000, [retrieved on Nov. 13, 2014], retrieved from the internet <URL:http://dl.acm.org/citation.cfm?id=335432>.
“SQL Subqueries”—Dec. 3, 2011, [retreieved on Nov. 7, 2014], retrieved from the internet <URL: https://web.archive.org/web/20111203033655/http://docs.oracle.com/cd/B28359_01/server.111/b28286/queries007.htm>.
“Caching Data with SqIDataSource Control”—Jul. 4, 2011, [retrieved on Nov. 12, 2014], retrieved from the internet <URL:https://web.archive.org/web/20110704142936/http://msdn.microsoft.com/en-us/library/z56y8ksb(v=VS.100).aspx>.
“SCD—Slowing Changing Dimensions in a DataWarehouse”—Aug. 7, 2011, [retrieved on Nov. 10, 2014], retrieved from the internet <URL:https://web.archive.org/web/20110807085325/http://etl-tools.info/en/scd.html>.
Buza, Antal. “Extension of CQL over Dynamic Databases.” J. UCS 12, No. 9 (2006): 1165-1176. [retrieved on Jun. 16, 2015], [retrieved on the internet URL<http://www.jucs.org/jucs_12_9/extension_of_cql_over%20/jucs_12_09_1165_1176_buza.pdf>].
SQL Tutorial-In, Tizag.com, http://web.archive.org/web/20090216215219/http://www.tizag.com/sgiTutorial/sqlin.php, Feb. 16, 2009, pp. 1-3.
U.S. Appl. No. 12/548,187, Final Office Action, dated Jun. 10, 2013, 18 pages.
U.S. Appl. No. 12/548,222, Notice of Allowance, dated Jul. 18, 2013, 12 pages.
U.S. Appl. No. 13/102,665, Final Office Action, dated Jul. 9, 2013, 17 pages.
U.S. Appl. No. 13/107,742, Final Office Action, dated Jul. 3, 2013, 19 pages.
Notice of Allowance for U.S. Appl. No. 11/977,437 dated Jul. 10, 2013, 10 pages.
Business Process Management (BPM), Datasheet [online]. IBM, [retrieved on Jan. 28, 2013]. Retrieved from the Internet: <URL: http://www-142.ibm.com/software/products/us/en/category/BPM-SOFTWARE>.
What is BPM? , Datasheet [online]. IBM, [retrieved on Jan. 28, 2013]. Retrieved from the Internet: <URL: http://www-01.ibm.com/software/info/bpm/whatis-bpm/>.
U.S Appl. No. 12/548,281, Final Office Action dated Oct. 10, 2013, 21 pages.
U.S Appl. No. 12/548,290, Notice of Allowance dated Sep. 11, 2013, 6 pages.
U.S Appl. No. 12/949,081, Final Office Action dated Aug. 27, 2013, 13 pages.
U.S Appl. No. 13/089,556, Final Office Action dated Aug. 29, 2013, 10 pages.
U.S Appl. No. 13/177,748, Non-Final Office Action dated Aug. 30, 2013, 24 pages.
U.S Appl. No. 13/193,377, Notice of Allowance dated Aug. 30, 2013, 19 pages.
Oracle Application Server, Enterprise Deployment Guide, 10g Release 3 (10.1.3.2.0), B32125-02, Oracle, Apr. 2007, 120 pages.
Oracle Database, SQL Language Reference 11 g Release 1 (11.1), B28286-02, Oracle, Sep. 2007, 1496 pages.
Esper Reference Documentation, Copyright 2007, Ver. 1.12.0, 2007, 158 pages.
Stream Query Repository: Online Auctions, at URL: http://www-db.stanford.edu/stream/sgr/onauc.html#queryspecsend, Dec. 2, 2002, 2 pages.
Esper Reference Documentation, Copyright 2008, ver. 2.0.0, 2008, 202 pages.
Oracle Database Data Cartridge Developer's Guide, B28425-03, 11 g Release 1 (11.1), Oracle, Mar. 2008, 372 pages.
Oracle Application Server, Administrator's Guide, 10g Release 3 (10.1.3.2.0), B32196-01, Oracle, Jan. 2007, 376 pages.
Oracle Application Server 10g, Release 2 and 3, New Features Overview, An Oracle White Paper, Oracle., Oct. 2005, 48 pages.
Oracle Database, SQL Reference, 10g Release 1 (10.1), Part No. B10759-01, Dec. 2003, pp. 7-1 to 7-17; 7-287 to 7-290; 14-61 to 14-74.
Complex Event Processing in the Real World, An Oracle White Paper., Sep. 2007, 13 pages.
Coral8 Complex Event Processing Technology Overview, Coral8, Inc., Make it Continuous, Copyright 2007 Coral8, Inc., 2007, pp. 1-8.
Creating WebLogic Domains Using the Configuration Wizard, BEA Products, Version 10.0, Dec. 2007, 78 pages.
Creating Weblogic Event Server Applications, BEA WebLogic Event Server, Version 2.0, Jul. 2007, 90 pages.
Dependency Injection, Wikipedia, printed on Apr. 29, 2011, at URL: D http:en.wikipedia.org/w/index.php? title=DependencLinjection&0ldid=260831402,, Dec. 30, 2008, pp. 1-7.
Deploying Applications to WebLogic Server, BEA WebLogic Server, ver. D 10.0, Mar. 30, 2007, 164 pages.
Developing Applications with Weblogic Server, BEA WebLogic Server, ver. D 10.0, Mar. 30, 2007, 254 pages.
EPL Reference, BEA WebLogic Event Server, ver. 2.0, Jul. 2007, 82 pages.
Esper Reference Documentation Version 3.1.0, EsperTech, retrieved from internet at URL: http://esper.codehaus.org/esper-3.1.0/doc/reference/en/pdf/esper_reference.pdf, 2009, 293 pages.
Fast Track Deployment and Administrator Guide for BEA WebLogic Server, BEA WebLogic Server 10.0 Documentation, printed on May 10, 2010, at URL:http://download.oracle.com/docs/cd/E13222_01 /wls/docs1 OO/quickstart/quick_start. html, May 10, 2010, 1page.
Getting Started with WebLogic Event Server, BEA WebLogic Event Serverver 2.0, Jul. 2007, 66 pages.
High Availability Guide, Oracle Application Server, 10g Release 3 (10.1.3.2.0), B32201-01, Jan. 2007, 314 pages.
Installing Weblogic Real Time, BEA WebLogic Real Time, Ver. 2.0, Jul. 2007, 64 pages.
Introduction to BEA WebLogic Server and BEA WebLogic Express, BEA WebLogic Server, Ver. 10.0, Mar. 2007, 34 pages.
Introduction to WebLogic Real Time, BEA WebLogic Real Time, ver. 2.0, Jul. 2007, 20 pages.
Jboss Enterprise Application Platform 4.3 Getting Started Guide CP03, for Use with Jboss Enterprise Application Platform 4.3 Cumulative Patch 3, Jboss a division of Red Hat, Red Hat Documentation Group, Copyright 2008, Red Hat, Inc., Sep. 2007, 68 pages.
Managing Server Startup and Shutdown, BEA WebLogic Server, ver. 10.0, Mar. 30, 2007, 134 pages.
Matching Behavior, .NET Framework Developer's Guide, Microsoft Corporation, Retrieved on: Jul. 1, 2008, URL: http://msdn.microsoft.com/en-us/library/Oyzc2ybO(pri nter).aspx, 2008, pp. 1-2.
New Project Proposal for Row Pattern Recognition—Amendment to SQL with Application to Streaming Data Queries, H2-2008-027, H2 Teleconference Meeting, Jan. 9, 2008, pp. 1-6.
Oracle CEP Getting Started, Release 11 gR1 (11.1.1) E14476-01, May 2009, 172 pages.
Oracle Complex Event Processing CQL Language Reference, 11g Release 1 (11.1.1) E12048-01, Apr. 2010, 540 pages.
OSGI Service Platform Core Specification, The OSGI Alliance, OSGI Alliance, ver. 4.1, release 4, Apr. 2007, 288 pages.
Release Notes, BEA WebLogic Event Server, Ver. 2.0, Jul. 2007, 8 pages.
Spring Dynamic Modules for OSGi Service Platforms product documentation, SpringSource, D, Jan. 2008, 71 pages.
Stream Base New and Noteworthy, Stream Base, Jan. 1, 2010, 878 pages.
Stream Query Repository: Online Auctions (CQL Queries)., Retrieved from: URL: http://www-db.stanford.edu/strem/sqr/cql/onauc.html, Dec. 2, 2002, 4 pages.
Stream: The Stanford Stream Data Manager, IEEE Data Engineering Bulletin., Mar. 2003, pp. 1-8.
Stream: The Stanford Stream Data Manager, Retrieved from: URL:http://infolab.stanford.edu/stream/, Jan. 5, 2006, pp. 1-9.
Understanding Domain Configuration, BEA WebLogic Server, Ver. 10.0, Mar. 30, 2007, 38 pages.
WebLogic Event Server Administration and Configuration Guide, BEA WebLogic Event D Server, Version. 2.0, Jul. 2007, 108 pages.
WebLogic Event Server Reference, BEA WebLogic Event Server, Version. 2.0, Jul. 2007, 52 pages.
Weblogic Server Performance and Tuning, BEA WebLogic Server, Ver. 10.0, Mar. 30, 2007, 180 pages.
WebSphere Application Server V6.1 Problem Determination: IBM Redpaper Collection, WebSphere Software, IBM/Redbooks, ,., Dec. 2007, 634 pages.
U.S. Appl. No. 10/948,523, Final Office Action dated Jul. 6, 2007, 37 pages.
U.S. Appl. No. 10/948,523, Non-Final Office Action dated Dec. 11, 2007, 47 pages.
U.S. Appl. No. 10/948,523, Notice of Allowance dated Dec. 1, 2010, 17 pages.
U.S. Appl. No. 10/948,523, Notice of Allowance dated Jul. 8, 2008, 30 pages.
U.S. Appl. No. 10/948,523, Office Action dated Jan. 22, 2007, 31 pages.
U.S. Appl. No. 10/948,523, Supplemental Notice of Allowance dated Jul. 17, 2008, 17 pages.
U.S. Appl. No. 10/948,523, Supplemental Notice of Allowance dated Aug. 25, 2008, 3 pages.
U.S. Appl. No. 11/601,415, Advisory Action dated Aug. 18, 2009, 3 pages.
U.S. Appl. No. 11/601,415, Final Office Action dated Jul. 2, 2012.
U.S. Appl. No. 11/601,415, Final Office Action dated May 27, 2009, 26 pages.
U.S. Appl. No. 11/601,415, Final Office Action dated Jun. 30, 2010, 45 pages.
U.S. Appl. No. 11/601,415, Non-Final Office Action dated Sep. 17, 2008, 10 pages.
U.S. Appl. No. 11/601,415, Non-Final Office Action dated Nov. 30, 2009, 32 pages.
U.S. Appl. No. 11/601,415, Office Action dated Dec. 9, 2011.
U.S. Appl. No. 11/873,407, Final Office Action dated Apr. 26, 2010, 11 pages.
U.S. Appl. No. 11/873,407, Non-Final Office Action dated Nov. 13, 2009, 7 pages.
U.S. Appl. No. 11/873,407, Notice of Allowance dated Nov. 10, 2010, 14 pages.
U.S. Appl. No. 11/873,407, Notice of Allowance dated Mar. 7, 2011, 8 pages.
U.S. Appl. No. 11/874,197, Final Office Action dated Aug. 12, 2011, 26 pages.
U.S. Appl. No. 11/874,197, Final Office Action dated Jun. 29, 2010, 17 pages.
U.S. Appl. No. 11/874,197, Non-Final Office Action dated Dec. 22, 2010, 22 pages.
U.S. Appl. No. 11/874,197, Office Action dated Nov. 10, 2009, 14 pages.
U.S. Appl. No. 11/874,202, Final Office Action dated Jun. 8, 2010, 200 pages.
U.S. Appl. No. 11/874,202, Non-Final Office Action dated Dec. 3, 2009, 20 pages.
U.S. Appl. No. 11/874,202, Notice of Allowance dated Mar. 31, 2011, 12 pages.
U.S. Appl. No. 11/874,202, Notice of Allowance dated Dec. 22, 2010, 29 pages.
U.S. Appl. No. 11/874,850, Notice of Allowance dated Jan. 27, 2010, 11 pages.
U.S. Appl. No. 11/874,850, Notice of Allowance dated Nov. 24, 2009, 17 pages.
U.S. Appl. No. 11/874,850, Notice of Allowance dated Dec. 11, 2009, 5 pages.
U.S. Appl. No. 11/874,896, Final Office Action dated Jul. 23, 2010, 28 pages.
U.S. Appl. No. 11/874,896, Non-Final Office Action dated Dec. 8, 2009, 19 pages.
U.S. Appl. No. 11/874,896, Non-Final Office Action dated Nov. 22, 2010, 25 pages.
U.S. Appl. No. 11/874,896, Notice of Allowance dated Jun. 23, 2011, 30 pages.
U.S. Appl. No. 11/927,681, Non-Final Office Action dated Mar. 24, 2011, 17 pages.
U.S. Appl. No. 11/927,681, Notice of Allowance dated Jul. 1, 2011, 8 pages.
U.S. Appl. No. 11/927,683, Final Office Action dated Sep. 1, 2011, 18 pages.
U.S. Appl. No. 11/927,683, Non-Final Office Action dated Mar. 24, 2011, 13 pages.
U.S. Appl. No. 11/927,683, Notice of Allowance dated Nov. 9, 2011, 10 pages.
U.S. Appl. No. 11/977,437, Final Office Action dated Apr. 8, 2010, 18 pages.
U.S. Appl. No. 11/977,437, Non-Final Office Action dated Oct. 13, 2009, 9 pages.
U.S. Appl. No. 11/977,437, Notice of Allowance dated Mar. 4, 2013, 9 pages.
U.S. Appl. No. 11/977,437, Office Action dated Aug. 3, 2012.
U.S. Appl. No. 11/977,439, Non-Final Office Action dated Apr. 13, 2010, 7 pages.
U.S. Appl. No. 11/977,439, Notice of Allowance dated Mar. 16, 2011, 10 pages.
U.S. Appl. No. 11/977,439, Notice of Allowance dated Aug. 18, 2010, 11 pages.
U.S. Appl. No. 11/977,439, Notice of Allowance dated Sep. 28, 2010, 6 pages.
U.S. Appl. No. 11/977,439, Notice of Allowance dated Nov. 24, 2010, 8 pages.
U.S. Appl. No. 11/977,440, Notice of Allowance dated Oct. 7, 2009, 6 pages.
U.S. Appl. No. 12/193,377, Final Office Action dated Jan. 17, 2013, 24 pages.
U.S. Appl. No. 12/395,871, Non-Final Office Action dated May 27, 2011, 7 pages.
U.S. Appl. No. 12/395,871, Notice of Allowance dated May 4, 2012, 27 pages.
U.S. Appl. No. 12/395,871, Office Action dated Oct. 19, 2011, 33 pages.
U.S. Appl. No. 12/396,008, Non-Final Office Action dated Jun. 8, 2011, 10 pages.
U.S. Appl. No. 12/396,008, Notice of Allowance dated Nov. 16, 2011, 5 pages.
U.S. Appl. No. 12/396,464, Final Office Action dated Jan. 16, 2013, 17 pages.
U.S. Appl. No. 12/396,464, Non-Final Office Action dated Sep. 7, 2012, 18 pages.
U.S. Appl. No. 12/506,891, Notice of Allowance dated Jul. 25, 2012, 8 pages.
U.S. Appl. No. 12/506,891, Office Action dated Dec. 14, 2011, 41 pages.
U.S. Appl. No. 12/506,905, Advisory Action dated Nov. 6, 2012, 6 pages.
U.S. Appl. No. 12/506,905, Notice of Allowance dated Dec. 14, 2012, 15 pages.
U.S. Appl. No. 12/506,905, Office Action dated Aug. 9, 2012, 42 pages.
U.S. Appl. No. 12/506,905, Office Action dated Mar. 26, 2012, 86 pages.
U.S. Appl. No. 12/534,384, Notice of Allowance dated May 7, 2013, 12 pages.
U.S. Appl. No. 12/534,384, Office Action dated Feb. 28, 2012, 38 pages.
U.S. Appl. No. 12/534,384, Office Action dated Feb. 12, 2013, 14 pages.
U.S. Appl. No. 12/534,398, Final Office Action dated Jun. 6, 2012, 27 pages.
U.S. Appl. No. 12/534,398, Notice of Allowance dated Nov. 27, 2012, 10 pages.
U.S. Appl. No. 12/534,398, Office Action dated Nov. 1, 2011, 32 pages.
U.S. Appl. No. 12/548,187, Non Final Office Action dated Sep. 27, 2011, 19 pages.
U.S. Appl. No. 12/548,187, Non-Final Office Action dated Apr. 9, 2013, 17 pages.
U.S. Appl. No. 12/548,187, Office Action dated Jun. 20, 2012, 31 pages.
U.S. Appl. No. 12/548,209, Notice of Allowance dated Oct. 24, 2012, 22 pages.
U.S. Appl. No. 12/548,209, Office Action dated Apr. 16, 2012, 40 pages.
U.S. Appl. No. 12/548,222, Non-Final Office Action dated Apr. 10, 2013, 16 pages.
U.S. Appl. No. 12/548,222, Non-Final Office Action dated Oct. 19, 2011, 19 pages.
U.S. Appl. No. 12/548,222, Office Action dated Jun. 20, 2012, 29 pages.
U.S. Appl. No. 12/548,281, Non-Final Office Action dated Apr. 12, 2013, 16 pages.
U.S. Appl. No. 12/548,281, Non-Final Office Action dated Oct. 3, 2011, 20 pages.
U.S. Appl. No. 12/548,281, Office Action dated Jun. 20, 2012, 29 pages.
U.S. Appl. No. 12/548,290, Final Office Action dated Jul. 30, 2012, 34 pages.
U.S. Appl. No. 12/548,290, Non-Final Office Action dated Oct. 3, 2011, 17 pages.
U.S. Appl. No. 12/548,290, Non-Final Office Action dated Apr. 15, 2013, 17 pages.
U.S. Appl. No. 12/874,197, Notice of Allowance dated Jun. 22, 2012.
U.S. Appl. No. 12/913,636, Final Office Action dated Jan. 8, 2013, 21 pages.
U.S. Appl. No. 12/913,636, Office Action dated Jun. 7, 2012.
U.S. Appl. No. 12/949,081, filed Nov. 18, 2010.
U.S. Appl. No. 12/949,081, Non-Final Office Action dated Jan. 9, 2013, 12 pages.
U.S. Appl. No. 12/957,194, filed Nov. 30, 2010.
U.S. Appl. No. 12/957,194, Non-Final Office Action dated Dec. 7, 2012, 11 pages.
U.S. Appl. No. 12/957,194, Notice of Allowance dated Mar. 20, 2013, 9 pages.
U.S. Appl. No. 12/957,201, filed Nov. 30, 2010.
U.S. Appl. No. 12/957,201, Final Office Action dated Apr. 25, 2013, 11 pages.
U.S. Appl. No. 12/957,201, Office Action dated Dec. 19, 2012, 15 pages.
U.S. Appl. No. 13/089,556, Non-Final Office Action dated Apr. 10, 2013, 10 pages.
U.S. Appl. No. 13/089,556, Office Action dated Nov. 6, 2012, 13 pages.
U.S. Appl. No. 13/089,556, filed Apr. 19, 2011.
U.S. Appl. No. 13/102,665, Office Action dated Feb. 1, 2013, 14 pages.
U.S. Appl. No. 13/107,742, Non-Final Office Action dated Feb. 14, 2013, 16 pages.
U.S. Appl. No. 13/184,528, Notice of Allowance dated Mar. 1, 2012.
U.S. Appl. No. 13/193,377, Office Action dated Jan. 17, 2013, 25 pages.
U.S. Appl. No. 13/193,377, Office Action dated Aug. 23, 2012, 48 pages.
U.S. Appl. No. 13/244,272, Final Office Action dated Mar. 28, 2013, 29 pages.
U.S. Appl. No. 13/244,272, Office Action dated Oct. 4, 2012.
U.S. Appl. No. 13/396,464, Office Action dated Sep. 7, 2012.
Abadi, et al., Yes Aurora: A Data Stream Management System, International Conference on Management of Data, Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, 2003, 4 pages.
Aho, et al., Efficient String Matching: An Aid to Bibliographic Search, Communications of the ACM, vol. 18, No. 6, Association for Computing Machinery, Inc., Jun. 1975, pp. 333-340.
Arasu, et al., An Abstract Semantics and Concrete Language for Continuous Queries over Streams and Relations, 9th International Workshop on Database programming languages, Sep. 2003, 11 pages.
Arasu, et al., An Abstract Semantics and Concrete Language for Continuous Queries over Streams and Relations, 9th International Workshop on Database programming languages, Sep. 2003, 12 pages.
Arasu, et al., CQL: A language for Continuous Queries over Streams and Relations, Lecture Notes in Computer Science vol. 2921, 2004, pp. 1-19.
Arasu, et al., STREAM: The Stanford Data Stream Management System, Department of Computer Science, Stanford University, 2004, p. 21.
Arasu, et al., The CQL Continuous Query Language: Semantic Foundations and Query Execution, Stanford University, The VLDB Journal—The International Journal on Very Large Data Bases, vol. 15, No. 2, Springer-Verlag New York, Inc., Jun. 2006, pp. 1-32.
Avnur, et al., Eddies: Continuously Adaptive Query Processing, In Proceedings of the 2000 ACM SIGMOD International Conference on Data, Dallas TX, May 2000, 12 pages.
Avnur, et al., Eddies: Continuously Adaptive Query Processing, slide show, believed to be prior to Oct. 17, 2007, 4 pages.
Babu, et al., Continuous Queries over Data Streams, SIGMOD Record, vol. 30, No. 3, Sep. 2001, pp. 109-120.
Bai, et al., A Data Stream Language and System Designed for Power and Extensibility, Conference on Information and Knowledge Management, Proceedings of the 15th ACM D International Conference on Information and Knowledge Management, Arlington, Virginia, Copyright 2006, ACM Press., Conference on Information and Knowledge Management, Proceedings of the 15th ACM D International Conference on Information and Knowledge Management, Arlington, Virginia, Copyright 2006, ACM Press., Nov. 5-11, 2006, 10 pages.
Bose, et al., A Query Algebra for Fragmented XML Stream Data, 9th International Conference on Data Base Programming Languages (DBPL), Sep. 2003, 11 pages.
Buza, Extension of CQL over Dynamic Databases, Journal of Universal Computer Science, vol. 12, No. 9, Sep. 28, 2006, pp. 1165-1176.
Carpenter, User Defined Functions, Retrieved from: URL: http://www.sqlteam.comitemprint.asp?ItemID=979, Oct. 12, 2000, 4 pages.
Chan, et al., Efficient Filtering of XML documents with Xpath expressions, VLDB Journal D, 2002, pp. 354-379.
Chandrasekaran, et al., TelegraphCQ: Continuous Dataflow Processing for an Uncertain World, Proceedings of CIDR, 2003, 12 pages.
Chen, et al., NiagaraCQ: A Scalable Continuous Query System for Internet Databases, Proceedings of the 2000 SIGMOD International Conference on Management of Data., May 2000, pp. 379-390.
Colyer, et al., Spring Dynamic Modules Reference Guide, Copyright, ver. 1.0.3, 2006-2008, 73 pages.
Colyer, et al., Spring Dynamic Modules Reference Guide, Ver. 1.1.3, 2006-2008, 96 pages.
Conway, An Introduction to Data Stream Query Processing, Truviso, Inc., URL: http://neilconway.org/talks/streamjntro.pdf, May 24, 2007, 71 pages.
Demers, et al., Towards Expressive Publish/Subscribe Systems, Proceedings of the 10th International Conference on Extending Database Technology (EDBT 2006),Munich, Germany, Mar. 2006, pp. 1-18.
Demichiel, et al., JSR 220: Enterprise JavaBeans™, EJB 3.0 Simplified API, EJB 3.0 Expert Group, Sun Microsystems, Ver. 3.0, May 2, 2006, 59 pages.
Deshpande, et al., Adaptive Query Processing, Slide show believed to be prior to Oct. 17, 2007, 27 pages.
Diao, et al., Query Processing for High-Volume XML Message Brokering, Proceedings of the 29th VLDB Conference, Berlin, Germany, 2003, 12 pages.
Diao, Query Processing for Large-Scale XML Message Brokering, University of California Berkeley, 2005, 226 pages.
Dindar, et al., Event Processing Support for Cross-Reality Environments, Pervasive Computing, IEEE CS, Jul.-Sep. 2009, Copyright 2009, IEEE, Jul.-Sep. 2009, pp. 2-9.
Fernandez, et al., Build your own XQuery processor, slide show, at URL: http://www.galaxquery.org/slides/edbt-summer-schooI2004.pdf, 2004, 116 pages.
Fernandez, et al., Implementing XQuery 1.0: The Galax Experience, Proceedings of the 29th VLDB Conference, Berlin, Germany, 2003, 4 pages.
Florescu, et al., The BEA/XQRL Streaming XQuery Processor, Proceedings of the 29th VLDB Conference, 2003, 12 pages.
Gilani, Design and implementation of stream operators, query instantiator and stream buffer manager, Dec. 2003, 137 pages.
Golab, et al., Issues in Data Stream Management, ACM SIGMOD Record, vol. 32, issue 2, ACM Press, Jun. 2003, pp. 5-14.
Golab, et al., Sliding Window Query Processing Over Data Streams, University of Waterloo, D Waterloo, Ont. Canada, Aug. 2006, 182 pages.
Gosling, et al., The Java Language Specification, Book, copyright, 3rd edition, FG, Sun Microsystems USA. D (due to size, reference will be uploaded in two parts), 1996-2005, 684 pages.
Hao, et al., Achieving high performance web applications by service and database replications at edge servers, Performance Computing and communications conference(IPCCC) IEEE 28th International, IEEE, Piscataway, NJ, USA, XP031622711, ISBN: 978-1-4244-5737-3, 2009, pp. 153-160.
Hopcroft, Introduction to Automata Theory, Languages, and Computation, Second Edition, Addison-Wesley, Copyright 2001, 524 pages.
Hulton, et al., Mining Time-Changing Data Stream, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2001, 10 pages.
Jin, et al., ARGUS: Efficient Scalable Continuous Query Optimization for Large-Volume Data Streams, 10th International Database Engineering and Applications Symposium (IDEAS'06), 2006, 7 pages.
Kawaguchi, et al., Java Architecture for XML Binding (JAXB) 2.2, Sun Microsystems, Inc., Dec. 10, 1999, 384 pages.
Knuth, et al., Fast Pattern Matching in Strings, Siam J Comput 6(2), Jun. 1977, pp. 323-350.
Lakshmanan, et al., On efficient matching of streaming XML documents and queries, 2002, 18 pages.
Lindholm, et al., Java Virtual Machine Specification, 2nd Edition Prentice Hall, Apr. 1999, 484 pages.
Liu, et al., Efficient XSLT Processing in Relational Database System, Proceeding of the 32nd. International Conference on Very Large Data Bases (VLDB), Sep. 2006, pp. 1106-1116.
Luckham, What's the Difference Between ESP and CEP? Complex Event Processing, downloaded, at URL:http://complexevents.com/?p=103, Apr. 29, 2011, 5 pages.
Madden, et al., Continuously Adaptive Continuous Queries (CACQ) over Streams, SIGMOD, Jun. 4-6, 2002, 12 pages.
Martin, et al., Finding Application Errors and Security Flaws Using PQL, a Program Query Language, OOPSLA'05, Oct. 16, 2005, pp. l-19.
Babcock, et al., Models and Issues in Data Streams, Proceedings of the 21st ACM SIGMOD-SIGACT-SIDART symposium on Principles of database systems, 2002, 30 pages.
Motwani, et al., Query Processing Resource Management, and Approximation in a Data 0 Stream Management System, Proceedings of CIDR, Jan. 2003, 12 pages.
Munagala, et al., Optimization of Continuous Queries with Shared Expensive Filters, Proceedings of the 26th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, Oct. 17, 2007, 14 pages.
Nah, et al., A Cluster-Based TMO-Structured Scalable Approach for Location Information Systems, Object-Oriented Real-Time Dependable Systems, 2003. WORDS 2003 Fall. Proceedings. Ninth IEEE International Workshop on Date of Conference: Oct. 1-3, 2003, pp. 225-233.
Novick, Creating a User Defined Aggregate with SQL Server 2005, URL: http://novicksoftware.com/Articles/sql-2005-product-user-defined-aggregate.html, 2005, 6 pages.
International Application No. PCT/US2011/052019, International Search Report and Written Opinion dated Nov. 17, 2011, 55 pages.
International Application No. PCT/US2012/034970, International Search Report and Written Opinion dated Jul. 16, 2012, 13 pages.
International Application No. PCT/US2012/036353, International Search Report and Written Opinion dated Sep. 12, 2012, 11 pages.
Peng, et al., Xpath Queries on Streaming Data, 2003, pp. 1-12.
Peterson, Petri Net Theory and the Modeling of Systems, Prentice Hall, 1981, 301 pages.
PostgresSQL, Manuals: PostgresSQL 8.2: Create Aggregate, believed to be prior to Apr. 21, 2007, 4 pages.
PostgresSQL, Documentation: Manuals: PostgresSQL 8.2: User-Defined Aggregates believed to be prior to Apr. 21, 2007, 4 pages.
Sadri, et al., Expressing and Optimizing Sequence Queries in Database Systems, ACM Transactions on Database Systems, vol. 29, No. 2, ACM Press, Copyright, Jun. 2004, pp. 282-318.
Sadtler, et al., WebSphere Application Server Installation Problem Determination, Copyright 2007, IBM Corp., 2007, pp. 1-48.
Sansoterra, Empower SQL with Java User-Defined Functions, ITJungle.com, Oct. 9, 2003, 9 pages.
Sharaf, et al., Efficient Scheduling of Heterogeneous Continuous Queries, VLDB, Sep. 12-15, 2006, pp. 511-522.
Stolze, et al., User-defined Aggregate Functions in DB2 Universal Database, Retrievd from: <http://www.128. ibm.com/deve1Operworks/d b2/1 ibrary/tachartic1e/0309stolze/0309stolze.html>, Sep. 11, 2003, 11 pages.
Stump, et al., Proceedings, The 2006 Federated Logic Conference, IJCAR '06 Workshop, PLPV '06: Programming Languages meets Program Verification, 2006, pp. 1-113.
Terry, et al., Continuous queries over append-only database, Proceedings of ACM SIGMOD, 1992, pp. 321-330.
Ullman, et al., Introduction to JDBC, Stanford University, 2005, 7 pages.
Vajjhala, et al., The Java Architecture for XML Binding (JAXB) 2.0, Sun Microsystem, D Inc., Final Release , Apr. 19, 2006, 384 pages.
Vijayalakshmi, et al., Processing location dependent continuous queries in distributed mobile databases using mobile agents, IET-UK International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007), Dec. 22, 2007, pp. 1023-1030.
W3C, XML Path Language (Xpath), W3C Recommendation, Version. 1.0, Retrieved from: URL: http://www.w3.org/TR/xpath, Nov. 16, 1999, 37 pages.
Wang, et al., Distributed continuous range query processing on moving objects, DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications, 2006, pp. 655-665.
White, et al., WebLogic Event Server: A Lightweight, Modular Application Server for Event Processing, 2nd International Conference on Distributed Event-Based Systems, Rome, Italy, Copyright 2004., Jul. 2-4, 2008, 8 pages.
Widom, et al., CQL: A Language for Continuous Queries over Streams and Relations, Oct. 17, 2007, 62 pages.
Widom, et al., The Stanford Data Stream Management System, PowerPoint Presentation, Oct. 17, 2007, 110 pages.
Wu, et al., Dynamic Data Management for Location Based Services in Mobile Environments, Database Engineering and Applications Symposium, Jul. 16, 2003, pp. 172-181.
Zemke, XML Query, mailed on Mar. 14, 2004, 29 pages.
U.S. Appl. No. 13/838,259, filed Mar. 15, 2013, Deshmukh et al.
U.S. Appl. No. 13/839,288, filed Mar. 15, 2013, Deshmukh et al.
Call User Defined Functions from Pig, Amazon Elastic MapReduce, Mar. 2009, 2 pages.
Strings in C, retrieved from the internet: <URL: https://web.archive.org/web/20070612231205/http:l/web.cs.swarthmore.edu/-newhall/unixhelp/C_strings.html> [retrieved on May 13, 2014], Swarthmore College, Jun. 12, 2007, 3 pages.
U.S. Appl. No. 11/874,197, Notice of Allowance dated Jun. 22, 2012, 20 pages.
U.S. Appl. No. 12/396,464, Final Office Action dated on May 16, 2014, 16 pages.
U.S. Appl. No. 12/548,187, Final Office Action dated Jun. 4, 2014, 64 pages.
U.S. Appl. No. 13/089,556, Final Office Action dated Jun. 13, 2014, 14 pages.
U.S. Appl. No. 13/107,742, Non-Final Office Action dated Jun. 19, 2014, 20 pages.
U.S. Appl. No. 13/244,272, Notice of Allowance dated Aug. 12, 2013, 12 pages.
International Application No. PCT/US2011/052019, International Preliminary Report on Patentability dated Mar. 28, 2013, 6 pages.
International Application No. PCT/US2012/034970, International Preliminary Report on Patentability dated Nov. 21, 2013, 7 pages.
International Application No. PCT/US2012/036353, International Preliminary Report on Patentability dated Nov. 28, 2013, 6 pages.
Bottom-up parsing, Wikipedia, downloaded from: http://en.wikipedia.org/wiki/Bottom-up_parsing, Sep. 8, 2014, pp. 1-2.
Branch Predication, Wikipedia, downloaded from: http://en.wikipedia.org/wiki/Branch_predication, Sep. 8, 2014, pp. 1-4.
Microsoft Computer Dictionary, 5th Edition, Microsoft Press, Redmond, WA, 2002, pp. 238-239 and 529.
Notice of Allowance for U.S. Appl. No. 13/089,556 dated Oct. 6, 2014, 9 pages.
U.S. Appl. No. 12/396,464, Notice of Allowance dated Sep. 3, 2014, 7 pages.
U.S. Appl. No. 12/548,187, Advisory Action dated Sep. 26, 2014, 6 pages.
U.S. Appl. No. 12/548,281, Final Office Action dated Aug. 13, 2014, 19 pages.
U.S. Appl. No. 12/913,636, Non-Final Office Action dated Jul. 24, 2014, 22 pages.
U.S. Appl. No. 12/957,201, Non-Final Office Action dated Jul. 30, 2014, 12 pages.
U.S. Appl. No. 13/764,560, Non-Final Office Action dated Sep. 12, 2014, 23 pages.
U.S. Appl. No. 13/770,969, Non-Final Office Action dated Aug. 7, 2014, 9 pages.
U.S. Appl. No. 14/302,031, Non-Final Office Action dated Aug. 27, 2014, 19 pages.
Abadi et al., Aurora: a new model and architecture for data stream management, The VLDB Journal the International Journal on Very Large Data Bases, vol. 12, No. 2, Aug. 1, 2003, pp. 120-139.
Balkesen et al., Scalable Data Partitioning Techniques for Parallel Sliding Window Processing over Data Streams, 8th International Workshop on Data Management for Sensor Networks, Aug. 29, 2011, pp. 1-6.
Chandrasekaran et al., PSoup: a system for streaming queries over streaming data, The VLDB Journal, The International Journal on Very Large Data Bases, vol. 12, No. 2, Aug. 1, 2003, pp. 140-156.
Dewson, Beginning SQL Server 2008 for Developers: From Novice to Professional, A Press, Berkeley, CA, 2008, pp. 337-349 and 418-438.
Harish D et al., Identifying robust plans through plan diagram reduction, PVLDB '08, Auckland, New Zealand, Aug. 23-28, pp. 1124-1140.
Krämer, Continuous Queries Over Data Streams—Semantics and Implementation, Fachbereich Mathematik und Informatik der Philipps-Universitat, Marburg, Germany, Retrieved from the Internet: URL:http://archiv.ub.uni-marburg.de/dissjz007/0671/pdfjdjk.pdf, Jan. 1, 2007; 313 pages.
International Application No. PCT/US2013/062047, International Search Report and Written Opinion dated Jul. 16, 2014, 12 pages.
International Application No. PCT/US2013/062050, International Search Report & Written Opinion dated Jul. 2, 2014, 13 pages.
International Application No. PCT/US2013/062052, International Search Report & Written Opinion dated Jul. 3, 2014, 12 pages.
International Application No. PCT/US2013/073086, International Search Report and Written Opinion dated Mar. 14, 2014.
International Application No. PCT/US2014/017061, International Search Report and Written Opinion dated Sep. 9, 2014, 12 pages.
Rao et al., Compiled Query Execution Engine using JVM, ICDE '06, Atlanta, GA, Apr. 3-7, 2006, 12 pages.
Ray et al., Optimizing complex sequence pattern extraction using caching, data engineering workshops (ICDEW)˜ 2011 IEEE 27th international conference on IEEE, Apr. 11, 2011, pp. 243-248.
Shah et al., Flux: an adaptive partitioning operator for continuous query systems, Proceedings of the 19th International Conference on Data Engineering, Mar. 5-8, 2003, pp. 25-36.
Stillger et al., LEO—DB2's LEarning Optimizer, Proc. of the VLDB, Roma, Italy, Sep. 2001, pp. 19-28.
U.S. Appl. No. 12/548,281, Non-Final Office Action dated Feb. 13, 2014, 16 pages.
U.S. Appl. No. 13/177,748, Final Office Action dated Mar. 20, 2014, 23 pages.
PCT Patent Application No. PCT/US2014/010832, International Search Report dated Apr. 3, 2014, 9 pages.
Cadonna et al., Efficient event pattern matching with match windows, Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (Aug. 2012), pp. 471-479.
Nichols et al., A faster closure algorithm for pattern matching in partial-order event data, IEEE International Conference on Parallel and Distributed Systems (Dec. 2007), pp. 1-9.
Oracle™ Fusion Middleware CQL Language Reference, 11g Release 1 (11.1.1.6.3) E12048-10, Aug. 2012, pp. 6-1 to 6-12.
Oracle™ Complex Event Processing CQL Language Reference, 11g Release 1 (11.1.1.4.0) E12048-04, Jan. 2011, pp. 6.1 to 6.12.
Oracle™ Complex Event Processing CQL Language Reference, 11g Release 1 (11.1.1) E12048-03, Apr. 2010, sections 18-4 to 18.4.2.
Pattern Recognition With MATCH_RECOGNIZE, Oracle™ Complex Event Processing CQL Language Reference, 11g Release 1 (11.1.1) E12048-03, May 2009, pp. 15.1 to 15.20.
Supply Chain Event Management: Real-Time Supply Chain Event Management, product information Manhattan Associates, 2009-2012.
What is BPM?, Datasheet [online]. IBM, [retrieved on Jan. 28, 2013]. Retrieved from the Internet: <URL: http://www-01.ibm.com/software/info/bpm/whatis-bpm/>.
U.S. Appl. No. 11/601,415, Non-Final Office Action dated Dec. 11, 2013, 58 pages.
U.S. Appl. No. 12/396,464, Non Final Office Action dated Dec. 31, 2013, 16 pages.
U.S. Appl. No. 12/548,281, Final Office Action dated Oct. 10, 2013, 21 pages.
U.S. Appl. No. 12/548,290, Notice of Allowance dated Sep. 11, 2013, 6 pages.
U.S. Appl. No. 12/949,081, Final Office Action dated Aug. 27, 2013, 13 pages.
U.S. Appl. No. 13/089,556, Final Office Action dated Aug. 29, 2013, 10 pages.
U.S. Appl. No. 13/089,556, Non-Final Office Action dated Jan. 9, 2014, 14 pages.
U.S. Appl. No. 13/177,748, Non-Final Office Action dated Aug. 30, 2013, 24 pages.
U.S. Appl. No. 13/193,377, Notice of Allowance dated Aug. 30, 2013, 19 pages.
Non-Final Office Action for U.S. Appl. No. 12/548,187 dated Feb. 6, 2014, 53 pages.
Agrawal et al. “Efficient pattern matching over event streams,” Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pp. 147-160 (Jun. 2008).
Chandramouli et al., High-Performance Dynamic Pattern Matching over Disordered Streams, Proceedings of the VLDB Endowment, vol. 3, Issue 1-2, Sep. 2010, pp. 220-231.
Chapple “Combining Query Results with the UNION Command,” ask.com Computing Databases, downloaded from: http://databases.about.com/od/sql/a/union.htm (no date, printed on Oct. 14, 2013).
Chui, WebSphere Application Server V6.1—Class loader problem determination, IBM.com, 2007.
Fantozzi, A Strategic Approach to Supply Chain Event Management, student submission for Masters Degree, Massachusetts Institute of Technology, Jun. 2003.
Komazec et al., Towards Efficient Schema-Enhanced Pattern Matching over RDF Data Streams, Proceedings of the 1st International Workshop on Ordering and Reasoning (OrdRing 2011), Bonn, Germany, Oct. 2011.
Ogrodnek, Custom UDFs and hive, Bizo development blog http://dev.bizo.com, Jun. 23, 2009, 2 pp.
Pradhan, Implementing and Configuring SAP® Event Management, Galileo Press, 2010, pp. 17-21.
Wilson et al., SAP Event Management, an Overview, Q Data USA, Inc., 2009.
Cranor et al. “Gigascope: a stream database for network applications,” Proceedings of the 2003 ACM SIGMOD international conference on Management of data, pp. 647-651 (Jun. 2003).
Oracle® Complex Event Processing EPL Language Reference 11g Release 1 (11.1.1.4.0), E14304-02, Jan. 2011, 80 pages.
De Castro Alves, A General Extension System for Event Processing Languages, DEBS '11, New York, NY, USA, Jul. 11-15, 2011, pp. 1-9.
Takenaka et al., A scalable complex event processing framework for combination of SQL-based continuous queries and C/C++ functions, FPL 2012, Oslo, Norway, Aug. 29-31, 2012, pp. 237-242.
Tomàs et al., RoSeS: A Continuous Content-Based Query Engine for RSS Feeds, DEXA 2011, Toulouse, France, Sep. 2, 2011, pp. 203-218.
Non-Final Office Action for U.S. Appl. No. 13/830,378 dated Feb. 25, 2015, 23 pages.
Non-Final Office Action for U.S. Appl. No. 13/830,129 dated Feb. 27, 2015, 19 pages.
International Application No. PCT/US2014/068641, International Search Report and Written Opinion dated Feb. 26, 2015, 11 pages.
European Patent Application No. 12783063.6, Extended Search Report dated Mar. 24, 2015, 6 pages.
Non-Final Office Action for U.S. Appl. No. 12/913,636 dated Apr. 1, 2015, 22 pages.
Final Office Action for U.S. Appl. No. 13/827,631 dated Apr. 3, 2015, 11 pages.
Notice of Allowance for U.S. Appl. No. 13/839,288 dated Apr. 3, 2015, 12 pages.
Notice of Allowance for U.S. Appl. No. 14/077,230 dated Apr. 16, 2015, 16 pages.
Final Office Action for U.S. Appl. No. 13/764,560 dated Apr. 15, 2015, 19 pages.
U.S. Appl. No. 12/949,081, Non-Final Office Action dated Jan. 28, 2015, 20 pages.
U.S. Appl. No. 12/957,201, Notice of Allowance dated Jan. 21, 2015, 5 pages.
U.S. Appl. No. 13/107,742, Final Office Action dated Jan. 21, 2015, 23 pages.
U.S. Appl. No. 13/177,748, Non-Final Office Action dated Feb. 3, 2015, 22 pages.
U.S. Appl. No. 13/770,961, Non-Final Office Action dated Feb. 4, 2015, 22 pages.
U.S. Appl. No. 13/770,969, Notice of Allowance dated Jan. 22, 2015, 5 pages.
U.S. Appl. No. 13/829,958, Non-Final Office Action dated Dec. 11, 2014, 15 pages.
U.S. Appl. No. 13/839,288, Non-Final Office Action dated Dec. 4, 2014, 30 pages.
U.S. Appl. No. 13/906,162, Non-Final Office Action dated Dec. 29, 2014, 10 pages.
U.S. Appl. No. PCT/US2014/010832, Written Opinion dated Dec. 15, 2014, 5 pages.
International Application No. PCT/US2014/010920, International Search Report and Written Opinion dated Dec. 15, 2014, 10 pages.
International Application No. PCT/US2014/017061, Written Opinion dated Feb. 3, 2015, 6 pages.
International Application No. PCT/US2014/039771, International Search Report and Written Opinion dated Sep. 24, 2014, 12 pages.
Tho et al. “Zero-latency data warehousing for heterogeneous data sources and continuous data streams,” 5th International Conference on Information Integrationand Web-based Applications Services (Sep. 2003) 12 pages.
Non-Final Office Action for U.S. Appl. No. 13/838,259 dated Oct. 24, 2014, 21 pages.
Notice of Allowance for U.S. Appl. No. 13/102,665 dated Nov. 24, 2014, 9 pages.
Non-Final Office Action for U.S. Appl. No. 13/827,631 dated Nov. 13, 2014, 10 pages.
Non-Final Office Action for U.S. Appl. No. 13/827,987 dated Nov. 6, 2014, 9 pages.
Non-Final Office Action for U.S. Appl. No. 11/601,415 dated Oct. 6, 2014, 18 pages.
Non-Final Office Action for U.S. Appl. No. 14/077,230 dated Dec. 4, 2014, 30 pages.
Non-Final Office Action for U.S. Appl. No. 13/828,640 dated Dec. 2, 2014, 11 pages.
Non-Final Office Action for U.S. Appl. No. 13/830,428 dated Dec. 5, 2014, 23 pages.
Notice of Allowance for U.S. Appl. No. 12/548,187 dated Aug. 17, 2015, 18 pages.
Notice of Allowance for U.S. Appl. No. 13/107,742 dated Jul. 8, 2015, 9 pages.
Non-Final Office Actio for U.S. Appl. No. 14/037,072 dated Jul. 9, 2015, 12 pages.
Non-Final Office Action for U.S. Appl. No. 14/036,659 dated Aug. 13, 2015, 33 pages.
Oracle Complex Event Processing Exalogic Performance Study, http://www.oracle.com/technetwork!middleware/complex-event-processing/overview/cepexalogicwhitepaperfinal-498043.pdf, Sep. 2011, pp. 1-16.
“Data stream management system”, Wikipedia, downloaded from en.wikipedia.org/wiki/Data_stream_management_system on Sep. 23, 2015, pp. 1-5.
Josifovsky, Vanja, et al., “Querying XML Streams”, The VLDB Journal, vol. 14, © 2005, pp. 197-210.
Purvee, Edwin Ralph, “Optimizing SPARQLeR Using Short Circuit Evaluation of Filter Clauses”, Master of Science Thesis, Univ. of Georgia, Athens, GA, © 2009, 66 pages.
Weidong, Yang, et al., “LeoXSS: An Efficient XML Stream System for Processing Complex XPaths”, CIT 2006, Seoul, Korea, © 2006, 6 pages.
Japan Patent Office office actions JPO patent application JP2013-529376 (dated Aug. 18, 2015).
Final Office Action for U.S. Appl. No. 13/177,748 dated Aug. 21, 2015, 24 pages.
Non-Final Office Action for U.S. Appl. No. 14/036,500 dated Aug. 14, 2015, 26 pages.
Notice of Allowance for U.S. Appl. No. 13/830,129 dated Sep. 22, 2015, 9 pages.
Final Office Action for U.S. Appl. No. 13/770,961 dated Aug. 31, 2015, 28 pages.
Non-Final Office Action for U.S. Appl. No. 13/764,560 dated Oct. 6, 2015, 18 pages.
Non-Final Office Action for U.S. Appl. No. 14/621,098 dated Oct. 15, 2015, 21 pages.
Notice of Allowance for U.S. Appl. No. 14/692,674 dated Oct. 15, 2015, 10 pages.
Notice of Allowance for U.S. Appl. No. 14/037,171 dated Oct. 15, 2015, 14 pages.
Final Office Action for U.S. Appl. No. 14/302,031 dated Apr. 22, 2015, 23 pages.
Non-Final Office Action for U.S. Appl. No. 14/692,674 dated Jun. 5, 2015, 22 pages.
Non-Final Office Action for U.S. Appl. No. 14/037,171 dated Jun. 3, 2015, 15 pages.
Non-Final Office Action for U.S. Appl. No. 14/830,735 dated May 26, 2015, 19 pages.
Final Office Action for U.S. Appl. No. 13/830,428 dated Jun. 4, 2015, 21 pages.
Non-Final Office Action for U.S. Appl. No. 14/838,259 dated Jun. 9, 2015, 37 pages.
Final Office Action for U.S. Appl. No. 14/906,162 dated Jun. 10, 2015, 10 pages.
Non-Final Office Action for U.S. Appl. No. 14/037,153 dated Jun. 19, 2015, 23 pages.
Final Office Action for U.S. Appl. No. 13/829,958 dated Jun. 19, 2015, 17 pages.
Final Office Action for U.S. Appl. No. 13/827,987 dated Jun. 19, 2015, 10 pages.
International Application No. PCT/US2014/039771, International Search Report and Written Opinion dated Apr. 29, 2015 6 pages.
International Application No. PCT/US2015/016346, International Search Report and Written Opinion dated May 4, 2015, 9 pages.
International Preliminary Report on Patentability dated Apr. 9, 2015 for PCT/US2013/062047, 10 pages.
International Preliminary Report on Patentability dated Apr. 9, 2015 for PCT/US2013/062052, 18 pages.
International Preliminary Report on Patentability dated May 28, 2015 for PCT/US2014/017061, 31 pages.
International Preliminary Report on Patentability dated Jun. 18, 2015 for PCT/US2013/073086, 7 pages.
International Preliminary Report on Patentability dated Jul. 29, 2015 for PCT/US2014/010920, 30 pages.
International Preliminary Report on Patentability dated Jul. 29, 2015 for PCT/US2014/039771, 24 pages.
China Patent Office office actions for patent application CN201280022008.7 (dated Dec. 3, 2015).
European Application No. 12783063.6, Office Action dated Nov. 11, 2015, 8 pages.
Notice of Allowance for U.S. Appl. No. 12/548,187, dated Feb. 2, 2016, 15 pages.
Notice of Allowance for U.S. Appl. No. 14/037,072 dated Feb. 16, 2016, 17 pages.
Final Office Action for U.S. Appl. No. 13/830,735 dated Dec. 21, 2015, 20 pages.
Notice of Allowance for U.S. Appl. No. 13/827,987 dated Jan. 4, 2016, 16 pages.
Notice of Allowance for U.S. Appl. No. 13/177,748 dated Jan. 6, 2016, 9 pages.
Notice of Allowance for U.S. Appl. No. 13/828,640 dated Jan. 6, 2016, 16 pages.
Non-Final Office Action for U.S. Appl. No. 13/830,428 dated Jan. 15, 2016, 25 pages.
Final Office Action for U.S. Appl. No. 14/037,153 dated Jan. 21, 2016, 31 pages.
Non-Final Office Action for U.S. Appl. No. 13/829,958 dated Feb. 1, 2016, 20 pages.
Non-Final Office Action for U.S. Appl. No. 13/827,631 dated Feb. 11, 2016, 12 pages.
Ghazal et al., Dynamic plan generation for parameterized queries, Jul. 2009, 7 pages.
Chaudhuri et al., Variance aware optimization of parameterized queries, Jun. 2010, 12 pages.
Seshadri et al., SmartCQL: Semantics to Handle Complex Queries over Data Streams, 2010, 5 pages.
International Search Report and Written Opinion dated Dec. 15, 2015 for PCT/US2015/051268, 17 pages.
“11 Oracle Event Processing NoSQL 1-20 Database Data Cartridge-Ilg Release 1 (11.1.1.7) 11,” Oracle Fusion Middleware CQL Language Reference for Oracle Event Processing 11g Release 1 (11.1.1.7), 4 pages (Sep. 25, 2013).
Oracle Event Processing Hadoop Data Cartridge-11g Release 1(11.1.1.7), Oracle Fusion Middleware CQL LanguageReference for Oracle Event Processing 11g Release 1 (11.1.1.7) 4 pages (Sep. 25, 2013).
Liu “Hbase Con 2014: HBase Design Patterns @Yahoo!” (May 5, 2014), 20 pages.
Hasan et al. “Towards unified and native enrichment in event processing systems,” Proceedings of the 7th ACM international conference on Distributed event-based systems, pp. 171-182 (Jun. 29, 2013).
Katsov “In-Stream Big Data Processing : Highly Scalable Blog” 20 pages (Aug. 20, 2013).
Katsov “In-Stream Big Data Processing : Highly Scalable Blog” 19 pages (Aug. 29, 2014).
Final Office Action for U.S. Appl. No. 13/830,759 dated Feb. 18, 2016, 18 pages.
Notice of Allowance for U.S. Appl. No. 13/770,961 dated Apr. 4, 2016, 8 pages.
Final Office Action for U.S. Appl. No. 13/838,259 dated Feb. 19, 2016, 47 pages.
Notice of Allowance for U.S. Appl. No. 13/906,162 dated Apr. 5, 2016, 7 pages.
Final Office Action for U.S. Appl. No. 14/036,500 dated Mar. 17, 2016, 34 pages.
Final Office Action for U.S. Appl. No. 13/764,560 dated Apr. 14, 2016, 20 pages.
Final Office Action for U.S. Appl. No. 14/621,098 dated Apr. 21, 2016, 16 pages.
Hirzel et al., “SPL Stream Processing Language Report”, IBM Research Report RC24897 (W0911-044), IBM Research Division, Thomas J. Watson Research center, Yorktown Heights, NY, Nov. 5, 2009, 19 pages.
Japan Patent Office office actions JPO patent application JP2014-509315 (dated Mar. 15, 2016).
Watanabe et al., Development of a Data Stream Integration System with a Multiple Query Optimizer, Journal articles of the 15th Data Engineering Workshop (DEWS2004), The Institute of Electronics, Information and Communication Engineers, Technical Committee on Data Engineering, Aug. 11, 2009, pp. 1-8.
Kuwata et al., Stream Data Analysis Application for Customer Behavior with Complex Event Processing, IEICE Technical Report, The Institute of Electronics, Information and Communication Engineers, Jun. 21, 2010, vol. 110, No. 107, pp. 13-18.
Kitagawa et al., Sensing Network, Information Processing, Information Processing Society of Japan, Sep. 15, 2010, vol. 51, No. 9, pp. 1119-1126.
Non-Final Office Action for U.S. Appl. No. 14/079,538 dated Oct. 22, 2015, 34 pages.
Non-Final Office Action for U.S. Appl. No. 13/906,162 dated Oct. 28, 2015, 11 pages.
Notice of Allowance for U.S. Appl. No. 14/302,031 dated Nov. 3, 2015, 18 pages.
Final Office Action for U.S. Appl. No. 12/949,081 dated Nov. 17, 2015, 19 pages.
China Patent Office office actions for patent application CN201180053021.4 (dated Oct. 28, 2015).
Notice of Allowance for U.S. Appl. No. 12/913,636 dated Oct. 27, 2015, 22 pages.
Final Office Action for U.S. Appl. No. 13/830,378 dated Nov. 5, 2015, 28 pages.
Non-Final Office Action for U.S. Appl. No. 11/601,415 dated Nov. 13, 2015, 18 pages.
Map Reduce, Wikipedia, The Free Encyclopedia, 2016, 11 pages.
Pig (programming tool), Wikipedia, The Free Encyclopedia, 2016, 4 pages.
U.S. Appl. No. 13/764,560, Notice of Allowance dated Sep. 30, 2016, 10 pages.
U.S. Appl. No. 14/079,538, Final Office Action dated Jul. 27, 2016, 28 pages.
U.S. Appl. No. 14/883,815, Notice of Allowance dated Aug. 30, 2016, 13 pages.
U.S. Appl. No. 13/827,631, Final Office Action dated Oct. 20, 2016, 12 pages.
Mahlke et al., Comparison of Full and Partial Predicated Execution Support for ILP Processors, ICSA '95, Santa Margherita Ligure, 1995, pp. 138-149.
Olston et al., Pig Latin, A Not-So-Foreign Language for Data Processing, 2008, 12 pages.
International Application No. PCT/US2015/016346, International Preliminary Report on Patentability dated Sep. 30, 2016, 6 pages.
International Application No. PCT/US2015/051268, Written Opinion dated Aug. 18, 2016, 7 pages.
Yang et al., Map-Reduce-Merge, Simplified Relational Data Processing on Large Clusters, 2007, 12 pages.
U.S. Appl. No. 13/828,640 Final Office Action, dated Jun. 17, 2015, 11 pages.
U.S. Appl. No. 13/829,958 Non-Final Office Action, dated Dec. 27, 2016, 20 pages.
U.S. Appl. No. 13/838,259 Non-Final Office Action, dated Jan. 4, 2017, 65 pages.
U.S. Appl. No. 14/610,971 Non-Final Office Action, dated Dec. 19, 2016, 10 pages.
U.S. Appl. No. 14/621,098 Non-Final Office Action, dated Nov. 14, 2016.
U.S. Appl. No. 15/015,933 Non-Final Office Action, dated Jan. 30, 2017, 11 pages.
U.S. Appl. No. 14/559,550 Non-Final Office Action, dated Jan. 27, 2017, 16 pages.
U.S. Appl. No. 15/003,646 Non-Final Office Action, dated Dec. 2, 2016, 9 pages.
U.S. Appl. No. 15/015,933, Non-Final Office Action dated Jan. 30, 2017, 11 pages.
U.S. Appl. No. 13/830,759, Non-Final Office Action dated Feb. 10, 2017, 23 pages.
U.S. Appl. No. 13/827,631, Non-Final Office Action dated Feb. 16, 2017, 16 pages.
International Application No. PCT/US2015/051268 International Preliminary Report on Patentability dated Dec. 8, 2016, 12 pages.
Bestehorn Fault-tolerant query processing in structured P2P-systems, Springer Science+Business Media LLC Distrib Parallel Databases 28:33-66 (May 8, 2010).
Cooperativesystems: “Combined WLAN and Inertial Indoor Pedestrian Positioning System” URL:https://www.youtube.com/watch?v=mEt88WaHZvU.
Frank et al “Development and Evaluation of a Combined WLAN & Inertial Indoor Pedestrian Positioning System” Proceedings of the 22nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2009). (Sep. 25, 2009) pp. 538-546.
Kramer “ Semantics and Implementation of Continuous Sliding Window Queries over Data Streams” ACM Transactions on Database Systems, vol. 34, pp. 4:1 to 4:49 (Apr. 2009).
Final Office Action for U.S. Appl. No. 13/830,428 dated May 26, 2016, 26 pages.
Final Office Action for U.S. Appl. No. 11/601,415 dated May 17, 2016, 17 pages.
Final Office Action for U.S. Appl. No. 14/036,659 dated Apr. 22, 2016, 38 pages.
Non-Final Office Action for U.S. Appl. No. 14/883,815 dated May 10, 2016, 32 pages.
Notice of Allowance for U.S. Appl. No. 12/949,081 dated May 3, 2016, 6 pages.
Final Office Action for U.S. Appl. No. 13/829,958 dated Jun. 30, 2016, 19 pages.
Final Office Action for U.S. Appl. No. 13/830,502 dated Jul. 6, 2016, 28 pages.
International Preliminary Report on Patentabiilty dated Jun. 16, 2016 for PCT/US2014/068641, 7 pages.
International Application No. PCT/RU2015/000468, International Search Report and Written Opinion dated Apr. 25, 2016, 9 pages.
International Application No. PCT/US2015/016346, International Search Report and Written Opinion dated May 24, 2016, 5 pages.
China Patent Office office action for patent application CN201180053021.4 (dated May 27, 2016).
U.S. Appl. No. 13/829,958, Final Office Action dated Jun. 26, 2017, 22 pages.
U.S. Appl. No. 13/830,378, Non-Final Office Action dated Jul. 5, 2017, 44 pages.
U.S. Appl. No. 13/838,259, Final Office Action dated Jul. 7, 2017, 69 pages.
U.S. Appl. No. 14/036,500, Notice of Allowance dated Jun. 30, 2017, 14 pages.
U.S. Appl. No. 14/036,559, Non-Final Office Action dated Jun. 2, 2017, 28 pages.
U.S. Appl. No. 14/559,550, Final Office Action dated Jul. 12, 2017, 21 pages.
U.S. Appl. No. 14/621,098, Notice of Allowance dated May 3, 2017, 9 pages.
U.S. Appl. No. 14/755,088, Non-Final Office Action dated Jun. 14, 2017, 13 pages.
U.S. Appl. No. 15/003,646, Notice of Allowance dated May 19, 2017, 16 pages.
U.S. Appl. No. 15/015,933, Notice of Allowance dated May 17, 2017, 16 pages.
U.S. Appl. No. 15/360,650, Notice of Allowance dated Jul. 24, 2017,13 pages.
Akama et al., Design and Evaluation of a Data Management System for WORM Data Processing, Journal of Information Processing, Information Processing Society of Japan, vol. 49, No. 2, Feb. 15, 2008, pp. 749-764.
Chinese Application No. 201380056012.X, Office Action dated Jun. 1, 2017, 22 pages (10 pages for the original document and 12 pages For the English translation).
Japanese Application No. 2015-534676, Office Action dated Jun. 27, 2017, 9 pages.
Sadana “Interactive Scatterplot for Tablets,” The 12th International Working Conference on Advanced Visual Interfaces, available from https://vimeo.com/97798460 (May 2014).
U.S. Appl. No. 13/830,428, Non-Final Office Action dated Mar. 22, 2017, 25 pages.
U.S. Appl. No. 14/036,500, Non-Final Office Action dated Feb. 9, 2017, 34 pages.
U.S. Appl. No. 14/079,538, Non-Final Office Action dated Mar. 31, 2017, 24 pages.
U.S. Appl. No. 15/360,650, Non-Final Office Action dated Mar. 9, 2017, 34 pages.
U.S. Appl. No. 13/830,735, Non-Final Office Action dated Apr. 4, 2017, 16 pages.
U.S. Appl. No. 15/177,147, Non-Final Office Action dated Apr. 7, 2017, 12 pages.
U.S. Appl. No. 14/866,512, Non-Final Office Action dated Apr. 10, 2017, 24 pages.
U.S. Appl. No. 14/610,971, Notice of Allowance dated Apr. 12, 2017,11 pages.
China Patent Application No. CN201480030482.3, Office Action dated Feb. 4, 2017, 5 pages.
U.S. Appl. No. 13/827,631, Final Office Action dated Aug. 30, 2017, 18 pages.
1 U.S. Appl. No. 13/830,428, Final Office Action dated Oct. 5, 2017, 33 pages.
U.S. Appl. No. 13/830,735, Final Office Action dated Sep. 29, 2017, 16 pages.
U.S. Appl. No. 13/830,759, Notice of Allowance dated Aug. 23, 2017, 14 pages.
U.S. Appl. No. 14/037,153, Non-Final Office Action dated Aug. 10, 2017, 45 pages.
U.S. Appl. No. 14/755,088, Notice of Allowance dated Oct. 11, 2017, 5 pages.
U.S. Appl. No. 14/861,687, Non-Final Office Action dated Oct. 11, 2017, 10 pages.
U.S. Appl. No. 14/866,512, Final Office Action dated Sep. 13, 2017, 25 pages.
U.S. Appl. No. 15/177,147, Non-Final Office Action dated Nov. 3, 2017, 6 pages.
Chinese Application No. 201380056017.2, Office Action dated Jul. 17, 2017, 25 pages (16 pages for the original document and 9 pages for the English translation).
Chinese Application No. 201380056099.0, Office Action dated Jul. 4, 2017, 26 pages (14 pages for the original document and 12 pages for the English translation).
Japanese Application No. 2015-534678, Office Action dated Aug. 29, 2017, 3 pages.
Japanese Application No. 2015-534680, Office Action dated Aug. 22, 2017, 10 pages.
European Patent Application EP14825489.9, Office Action dated Jul. 28, 2017, 7 pages.
U.S. Appl. No. 13/829,958, Non-Final Office Action dated Jan. 8, 2018, 22 pages.
U.S. Appl. No. 13/830,735, Notice of Allowance dated Jan. 26, 2018, 9 pages.
U.S. Appl. No. 13/838,259, Non-Final Office Action dated Nov. 27, 2017, 69 pages.
U.S. Appl. No. 14/036,659, Notice of Allowance dated Nov. 30, 2017, 13 pages.
U.S. Appl. No. 14/079,538, Final Office Action dated Nov. 16, 2017, 26 pages.
U.S. Appl. No. 14/559,550, Notice of Allowance dated Dec. 5, 2017, 6 pages.
U.S. Appl. No. 14/973,377, Non-Final Office Action dated Nov. 30, 2017, 17 pages.
U.S. Appl. No. 14/866,512, Notice of Allowance dated Feb. 15, 2018, 5 pages.
Chinese Application No. 201480004736.4, Office Action dated Nov. 29, 2017, 13 pages (7 pages of English translation and 6 pages Of Original document).
Japanese Application No. 2015-534676, Office Action dated Jan. 23, 2018, 9 pages.
Japanese Application No. 2015-552781, Office Action dated Nov. 21, 2017, 3 pages.
Japanese Application No. 2015-558217, Office Action dated Jan. 9, 2018, 3 pages.
Japanese Application No. 2015-552765, Office Action dated Dec. 5, 2017, 2 pages.
Chinese Application No. CN201380063379.4, Office Action dated Feb. 2, 2018 12 pages with translation..
International Patent Application PCT/RU2015/000468, International Preliminary Report on Patentability dated Feb. 8, 2018, 9 pages.
U.S. Appl. No. 13/827,631, Notice of Allowance dated Mar. 13, 2018, 10 pages.
U.S. Appl. No. 15/177,147, Notice of Allowance dated Mar. 22, 2018, 7 pages.
U.S. Appl. No. 13/830,428, Notice of Allowance dated Apr. 2, 2018, 9 pages.
Related Publications (1)
Number Date Country
20140095473 A1 Apr 2014 US
Provisional Applications (1)
Number Date Country
61707641 Sep 2012 US