Patent Grant
9665088
This patent relates generally to process plants and to process control systems, and more particularly, to the storage and management of big data in process plants and in process control system.
Distributed process control systems, like those used in chemical, petroleum or other process plants, typically include one or more process controllers communicatively coupled to one or more field devices via analog, digital or combined analog/digital buses, or via a wireless communication link or network. The field devices, which may be, for example, valves, valve positioners, switches and transmitters (e.g., temperature, pressure, level and flow rate sensors), are located within the process environment and generally perform physical or process control functions such as opening or closing valves, measuring process parameters, etc. to control one or more process executing within the process plant or system. Smart field devices, such as the field devices conforming to the well-known Fieldbus protocol may also perform control calculations, alarming functions, and other control functions commonly implemented within the controller. The process controllers, which are also typically located within the plant environment, receive signals indicative of process measurements made by the field devices and/or other information pertaining to the field devices and execute a controller application that runs, for example, different control modules which make process control decisions, generate control signals based on the received information and coordinate with the control modules or blocks being performed in the field devices, such as HART®, WirelessHART®, and FOUNDATION® Fieldbus field devices. The control modules in the controller send the control signals over the communication lines or links to the field devices to thereby control the operation of at least a portion of the process plant or system.
Information from the field devices and the controller is usually made available over a data highway to one or more other hardware devices, such as operator workstations, personal computers or computing devices, data historians, report generators, centralized databases, or other centralized administrative computing devices that are typically placed in control rooms or other locations away from the harsher plant environment. Each of these hardware devices typically is centralized across the process plant or across a portion of the process plant. These hardware devices run applications that may, for example, enable an operator to perform functions with respect to controlling a process and/or operating the process plant, such as changing settings of the process control routine, modifying the operation of the control modules within the controllers or the field devices, viewing the current state of the process, viewing alarms generated by field devices and controllers, simulating the operation of the process for the purpose of training personnel or testing the process control software, keeping and updating a configuration database, etc. The data highway utilized by the hardware devices, controllers and field devices may include a wired communication path, a wireless communication path, or a combination of wired and wireless communication paths.
As an example, the DeltaV™ control system, sold by Emerson Process Management, includes multiple applications stored within and executed by different devices located at diverse places within a process plant. A configuration application, which resides in one or more workstations or computing devices, enables users to create or change process control modules and download these process control modules via a data highway to dedicated distributed controllers. Typically, these control modules are made up of communicatively interconnected function blocks, which are objects in an object-oriented programming protocol that perform functions within the control scheme based on inputs thereto and that provide outputs to other function blocks within the control scheme. The configuration application may also allow a configuration designer to create or change operator interfaces which are used by a viewing application to display data to an operator and to enable the operator to change settings, such as set points, within the process control routines. Each dedicated controller and, in some cases, one or more field devices, stores and executes a respective controller application that runs the control modules assigned and downloaded thereto to implement actual process control functionality. The viewing applications, which may be executed on one or more operator workstations (or on one or more remote computing devices in communicative connection with the operator workstations and the data highway), receive data from the controller application via the data highway and display this data to process control system designers, operators, or users using the user interfaces, and may provide any of a number of different views, such as an operator's view, an engineer's view, a technician's view, etc. A data historian application is typically stored in and executed by a data historian device that collects and stores some or all of the data provided across the data highway while a configuration database application may run in a still further computer attached to the data highway to store the current process control routine configuration and data associated therewith. Alternatively, the configuration database may be located in the same workstation as the configuration application.
The architecture of currently known process control plants and process control systems is strongly influenced by limited controller and device memory, communication bandwidth and controller and device processor capability. For example, in currently known process control system architectures, the use of dynamic and static non-volatile memory in the controller is usually minimized or, at the least, managed carefully. As a result, during system configuration (e.g., a priori), a user typically must choose which data in the controller is to be archived or saved, the frequency at which it will be saved, and whether or not compression is used, and the controller is accordingly configured with this limited set of data rules. Consequently, data which could be useful in troubleshooting and process analysis is often not archived, and if it is collected, the useful information may have been lost due to data compression.
Additionally, to minimize controller memory usage in currently known process control systems, selected data that is to be archived or saved (as indicated by the configuration of the controller) is reported to the workstation or computing device for storage at an appropriate data historian or data silo. The current techniques used to report the data poorly utilizes communication resources and induces excessive controller loading. Additionally, due to the time delays in communication and sampling at the historian or silo, the data collection and timestamping is often out of sync with the actual process.
Similarly, in batch process control systems, to minimize controller memory usage, batch recipes and snapshots of controller configuration typically remain stored at a centralized administrative computing device or location (e.g., at a data silo or historian), and are only transferred to a controller when needed. Such a strategy introduces significant burst loads in the controller and in communications between the workstation or centralized administrative computing device and the controller.
Furthermore, the capability and performance limitations of relational databases of currently known process control systems, combined with the previous high cost of disk storage, play a large part in structuring data into independent entities or silos to meet the objectives of specific applications. For example, within the DeltaV™ system, the archiving of process models, continuous historical data, and batch and event data are saved in three different application databases or silos of data. Each silo has a different interface to access the data stored therein.
Structuring data in this manner creates a barrier in the way that historized data is accessed and used. For example, the root cause of variations in product quality may be associated with data in more than one of these data silos. However, because of the different file structures of the silos, it is not possible to provide tools that allow this data to be quickly and easily accessed for analysis. Further, audit or synchronizing functions must be performed to ensure that data across different silos is consistent.
The limitations of currently known process plants and process control system discussed above and other limitations may undesirably manifest themselves in the operation and optimization of process plants or process control systems, for instance, during plant operations, trouble shooting, and/or predictive modeling. For example, such limitations force cumbersome and lengthy work flows that must be performed in order to obtain data for troubleshooting and generating updated models. Additionally, the obtained data may be inaccurate due to data compression, insufficient bandwidth, or shifted timestamps.
“Big data” generally refers to a collection of one or more data sets that are so large or complex that traditional database management tools and/or data processing applications (e.g., relational databases and desktop statistic packages) are not able to manage the data sets within a tolerable amount of time. Typically, applications that use big data are transactional and end-user directed or focused. For example, web search engines, social media applications, marketing applications and retail applications may use and manipulate big data. Big data may be supported by a distributed database which allows the parallel processing capability of modern multi-process, multi-core servers to be fully utilized.
Current techniques for storing, accessing, and processing big data, and especially big data associated with process plants and process control systems, are inefficient. For example, various existing process plants use relational databases configured to store process control data which, in some cases, results in too much allocated storage and long retrieval times. Further, the storage of continuous historical data does not enable users or administrators to efficiently or effectively process trends or identify parameters, or combinations of parameters, from multiple data entries. Accordingly, there is an opportunity to develop techniques to more effectively and efficiently organize, process, and manage big data associated with process plants and process control systems.
A process control system or plant provides an infrastructure for supporting large-scale data mining and data analytics of process control data. A process control data network incorporates a big data schema which stores process control data and attributes thereof using lightweight non-relational database storage techniques. Using these techniques, the big data schema need not allocate storage for various process control attributes that are not present in the process control data. Further, the big data schema organizes the process control data into tables having rowkeys and column families to enable users and administrators to efficiently locate, access, and analyze the stored data.
The big data schema may create the rowkeys and the column families using various combinations of the process control data and attributes thereof. Generally, the rowkeys are unique key values that organize the data within the big data schema and that users may use to query and retrieve specific data. For example, some rowkeys incorporate timestamps (or portions thereof) corresponding to when the process control data is recorded. Each column family includes one or more column qualifiers that the big data schema creates using process control data attributes. The big data schema stores relevant measurements or values based on the corresponding rowkeys and column qualifiers. Some rowkeys may have multiple associated measurements (and multiple associated column qualifiers), thus resulting in a three-dimensional storage schema.
The big data schema also includes techniques for periodically creating and storing “snapshot” data corresponding to the underlying stored data. For example, for every elapsed minute, the big data schema may calculate and store the minimum, maximum, mean, and standard deviation for the underlying data having a timestamp within that minute. The big data schema thus enables a user to access specified data (e.g., a specific process variable) over a specified time period (e.g., hourly, weekly, monthly). The big data schema then presents the data to the user in an interface to enable the user to efficiently and effectively assess the snapshot data and perform desired data analyses.
The process control system big data network 100 may collect and store any type of data related to the process control system 10. For example, the process control system big data network 100 collects and stores real-time process data such as continuous, batch, measurement and event data that is generated while a process is being controlled in the process plant 10 (and, in some cases, is indicative of an effect of a real-time execution of the process). Process definition, arrangement or set-up data such as configuration data and/or batch recipe data, as well as data corresponding to the configuration, execution and results of process diagnostics may also be collected and stored. Of course, other types of process data may also be collected and stored.
In addition, the process control system big data network 100 may collect and store data highway traffic and network management data of the backbone 105 and of various other communication networks of the process plant 10. Still further, the process control system big data network 100 may collect and store user-related data such as data related to user traffic, login attempts, queries and instructions, as well as text data (e.g., logs, operating procedures, manuals, etc.), spatial data (e.g., location-based data) and multi-media data (e.g., closed circuit TV, video clips, etc.).
In addition, the process control system big data network 100 may collect and store data that is related to the process plant 10 (e.g., to physical equipment included in the process plant 10 such as machines and devices) but that may not be generated by applications that directly configure, control, or diagnose a process. For example, the process control system big data network 100 may collect and store vibration data, steam trap data, data indicative of a value of a parameter corresponding to plant safety (e.g., corrosion data, gas detection data, etc.), and/or data indicative of an event corresponding to plant safety. In some cases, the process control system big data network 100 may collect and store data corresponding to the health of machines, plant equipment and/or devices. For example, equipment data (e.g., pump health data determined based on vibration data and other data) may be collected. In some cases, the process control system big data network 100 may collect and store data corresponding to the configuration, execution and results of equipment, machine, and/or device diagnostics.
Moreover, the process control system big data network 100 may collect and store data generated by or transmitted to entities external to the process plant 10, such as data related to costs of raw materials, expected arrival times of parts or equipment, weather data, and other external data. If desired, all data that is generated, received, or observed by all nodes 108 that are communicatively connected to the network backbone 105 may be collected and caused to be stored at the process control system big data appliance 102.
As illustrated in
Process Control Big Data Network Nodes
As illustrated in
At least one of the provider devices 110 may communicatively connect to the process control big data network backbone 105 in a direct manner. In addition, at least one of the provider devices 110 may communicatively connect to the backbone 105 in an indirect manner. For example, a wireless field device may communicatively connect to the backbone 105 via a router, and access point, and a wireless gateway. Typically, provider devices 110 do not have an integral user interface, although some of the provider devices 100 may have the capability to be in communicative connection with a user computing device or user interface, e.g., by communicating over a wired or wireless communication link, or by plugging a user interface device into a port of the provider device 110.
As illustrated in
Of course, the plurality of nodes 108 of the process control big data network 100 is not limited to only provider nodes 110 and user interface nodes 112. One or more other types of nodes 115 may optionally be included in the plurality of nodes 108. For example, a node of a system that is external to the process plant 10 (e.g., a lab system or a materials handling system) may communicatively connect to the network backbone 105 of the system 100. A node or device 115 may communicatively connect to the backbone 105 via a direct or an indirect connection. In addition, a node or device 115 may communicatively connect to the backbone 105 via a wired or a wireless connection.
At least some of the nodes 108 of the process control system big data network 100 may include an integrated firewall. Further, any number of the nodes 108 (e.g., zero nodes, one node, or more than one node) may each include respective memory storage (denoted in
Any number of the nodes 108 (e.g., zero nodes, one node, or more than one node) may each include respective multi-core hardware (e.g., a multi-core processor or another type of parallel processor), as denoted in
It is noted, though, that while
Examples of real-time data that may be cached or collected by provider nodes or devices 110 may include measurement data, configuration data, batch data, event data, maintenance data, and/or continuous data. For instance, real-time data corresponding to configurations, batch recipes, setpoints, outputs, rates, control actions, diagnostics, alarms, events and/or changes thereto may be collected. Other examples of real-time data may include process models, statistics, status data, and network and plant management data.
Examples of real-time data that user interface nodes or devices 112 may cache or collect may include, for example, user logins, user queries, data captured by a user (e.g., by camera, audio, or video recording device), user commands, creation, modification or deletion of files, a physical or spatial location of a user interface node or device, results of a diagnostic or test performed by the user interface device 112, and other actions or activities initiated by or related to a user interacting with a user interface node 112.
Collected data may be dynamic or static data. Collected data may include, for example, database data, streaming data, and/or transactional data. Generally, any data that a node 108 generates, receives, or observes may be collected or cached with a corresponding timestamp or indication of a time of collection/caching. In some cases, all data that a node 108 generates, receives, or observes is collected or cached in its memory storage (e.g., high density memory storage MX) with a respective indication of a time of each datum's collection/caching (e.g., a timestamp).
Each of the nodes 110, 112 (and, optionally, at least one of the other nodes 115) may be configured to automatically collect or cache real-time data and to cause the collected/cached data to be delivered to the big data appliance 102 and/or to other nodes 108 without requiring lossy data compression, data sub-sampling, or configuring the node for data collection purposes. Unlike prior art process control systems, the identity of data that is collected at the nodes or devices 108 of the process control system big data network 100 need not be configured into the devices 108 a priori. Further, the rate at which data is collected at and delivered from the nodes 108 also need not be configured, selected or defined. Instead, the nodes 110, 112 (and, optionally, at least one of the other nodes 115) of the process control big data system 100 may automatically collect all data that is generated by, received at, or obtained by the node at the rate at which the data is generated, received or obtained, and may cause the collected data to be delivered in high fidelity (e.g., without using lossy data compression or any other techniques that may cause loss of original information) to the process control big data appliance 102 and, optionally, to other nodes 108 of the network 100.
A detailed block diagram illustrating example provider nodes 110 connected to process control big data network backbone 105 is illustrated in
The controller 11, which may be, by way of example, the DeltaV™ controller sold by Emerson Process Management, may operate to implement a batch process or a continuous process using at least some of the field devices 15-22 and 40-46. The controller 11 may communicatively connect to the field devices 15-22 and 40-46 using any desired hardware and software associated with, for example, standard 4-20 mA devices, I/O cards 26, 28, and/or any smart communication protocol such as the FOUNDATION® Fieldbus protocol, the HART® protocol, the WirelessHART® protocol, etc. The controller 11 may additionally or alternatively communicatively connect with at least some of the field devices 15-22 and 40-46 using the big data network backbone 105. In the system illustrated in
The controller 11 of
The controller 11 may also implement a control strategy using what are commonly referred to as function blocks, wherein each function block is an object or other part (e.g., a subroutine) of an overall control routine and operates in conjunction with other function blocks (via communications called links) to implement process control loops within the process control system 10. Control based function blocks typically perform one of an input function, such as that associated with a transmitter, a sensor or other process parameter measurement device, a control function, such as that associated with a control routine that performs PID, fuzzy logic, etc. control, or an output function which controls the operation of some device, such as a valve, to perform some physical function within the process control system 10. Of course, hybrid and other types of function blocks exist. Function blocks may be stored in and executed by the controller 11, which is typically the case when these function blocks are used for, or are associated with standard 4-20 mA devices and some types of smart field devices such as HART devices, or may be stored in and implemented by the field devices themselves, which can be the case with Fieldbus devices. The controller 11 may include one or more control routines 38 that may implement one or more control loops. Each control loop is typically referred to as a control module, and may be performed by executing one or more of the function blocks.
The wired field devices 15-22 may be any types of devices, such as sensors, valves, transmitters, positioners, etc., while the I/O cards 26 and 28 may be any types of I/O devices conforming to any desired communication or controller protocols. As illustrated in
In the system illustrated in
The wireless gateway 35 is an example of a provider device 110 that may provide access to various wireless devices 40-58 of a wireless communication network 70. In particular, the wireless gateway 35 provides communicative coupling between the wireless devices 40-58, the wired devices 11-28, and/or other nodes 108 of the process control big data network 100 (including the controller 11 of
The wireless gateway 35 provides communicative coupling, in some cases, by the routing, buffering, and timing services to lower layers of the wired and wireless protocol stacks (e.g., address conversion, routing, packet segmentation, prioritization, etc.) while tunneling a shared layer or layers of the wired and wireless protocol stacks. In other cases, the wireless gateway 35 may translate commands between wired and wireless protocols that do not share any protocol layers. In addition to protocol and command conversion, the wireless gateway 35 may provide synchronized clocking used by time slots and superframes (sets of communication time slots spaced equally in time) of a scheduling scheme associated with the wireless protocol implemented in the wireless network 70. Furthermore, the wireless gateway 35 may provide network management and administrative functions for the wireless network 70, such as resource management, performance adjustments, network fault mitigation, monitoring traffic, security, and the like. The wireless gateway 35 may be a node 110 of the process control system big data network 100.
Similar to the wired field devices 15-22, the wireless field devices 40-46 of the wireless network 70 may perform physical control functions within the process plant 10, e.g., opening or closing valves or taking measurements of process parameters. The wireless field devices 40-46, however, are configured to communicate using the wireless protocol of the network 70. As such, the wireless field devices 40-46, the wireless gateway 35, and other wireless nodes 52-58 of the wireless network 70 are producers and consumers of wireless communication packets.
In some scenarios, the wireless network 70 may include non-wireless devices. For example, a field device 48 of
Accordingly,
The provider nodes 110 of the process control big data network 100, though, may also include other nodes that communicate using other wireless protocols. For example, the provider nodes 110 may include one or more wireless access points 72 that utilize other wireless protocols, such as WiFi or other IEEE 802.11 compliant wireless local area network protocols, mobile communication protocols such as WiMAX (Worldwide Interoperability for Microwave Access), LTE (Long Term Evolution) or other ITU-R (International Telecommunication Union Radiocommunication Sector) compatible protocols, short-wavelength radio communications such as near field communications (NFC) and Bluetooth, or other wireless communication protocols. Typically, such wireless access points 72 allow handheld or other portable computing devices (e.g., user interface devices 112) to communicate over a respective wireless network that is different from the wireless network 70 and that supports a different wireless protocol than the wireless network 70. In some scenarios, in addition to portable computing devices, one or more process control devices (e.g., controller 11, field devices 15-22, or wireless devices 35, 40-58) may also communicate using the wireless protocol supported by the access points 72.
Additionally or alternatively, the provider nodes 110 may include one or more gateways 75, 78 to systems that are external to the immediate process control system 10. Typically, such systems are customers or suppliers of information generated or operated on by the process control system 10. For example, a plant gateway node 75 may communicatively connect the immediate process plant 10 (having its own respective process control big data network backbone 105) with another process plant having its own respective process control big data network backbone. If desired, a single process control big data network backbone 105 may service multiple process plants or process control environments.
In another example, a plant gateway node 75 may communicatively connect the immediate process plant 10 to a legacy or prior art process plant that does not include a process control big data network 100 or backbone 105. In this example, the plant gateway node 75 may convert or translate messages between a protocol utilized by the process control big data backbone 105 of the plant 10 and a different protocol utilized by the legacy system (e.g., Ethernet, Profibus, Fieldbus, DeviceNet, etc.).
The provider nodes 110 may include one or more external system gateway nodes 78 to communicatively connect the process control big data network 100 with the network of an external public or private system, such as a laboratory system (e.g., Laboratory Information Management System or LIMS), an operator rounds database, a materials handling system, a maintenance management system, a product inventory control system, a production scheduling system, a weather data system, a shipping and handling system, a packaging system, the Internet, another provider's process control system, or other external systems.
As previously discussed, one or more of the provider nodes 110 may include a respective multi-core processor PMCX, a respective high density memory storage MX, or both a respective multi-core processor PMCX and a respective high density memory storage MX (denoted in
For nodes 110 that have a direct connection with the backbone 105 (e.g., the controller 11, the plant gateway 75, the wireless gateway 35), the respective cached or collected data may be transmitted directly from the node 110 to the process control big data appliance 102 via the backbone 105. For at least some of the nodes 110, though, the collection and/or caching may be leveled or layered, so that cached or collected data at a node that is further downstream (e.g., is further away) from the process control big data appliance 102 is intermediately cached at a node that is further upstream (e.g., is closer to the big data appliance 102).
To illustrate layered or leveled data caching, an example scenario is provided. In this example scenario, referring to
In a second example scenario of layered or leveled caching, the controller 11 controls a process using wired field devices (e.g., one or more of the devices 15-22) and at least one wireless field device (e.g., wireless field device 44). In a first implementation of this second example scenario, the cached or collected data at the wireless device 44 is delivered and/or streamed directly to the controller 11 from the wireless device 44 (e.g., via the big data network 105), and is stored at the controller cache M6 along with data from other devices or nodes that are downstream from the controller 11. The controller 11 may periodically deliver or stream the data stored in its cache M6 to the process control big data appliance 102.
In another implementation of this second example scenario, the cached or collected data at the wireless device 44 may be ultimately delivered to the process control big data appliance 102 via an alternate leveled or layered path, e.g., via the device 42a, the router 52a, the access point 55a, and the wireless gateway 35. In this case, at least some of the nodes 41a, 52a, 55a or 35 of the alternate path may cache data from downstream nodes and may periodically deliver or stream its cached data to a node that is further upstream.
Accordingly, the process control system big data network 100 may cache different types of data at different nodes using different layering or leveling arrangements. For example, data corresponding to controlling a process may be cached and delivered in a layered manner using provider devices 110 whose primary function is control (e.g., field devices, I/O cards, controllers), whereas data corresponding to network traffic measurement may be cached and delivered in a layered manner using provider devices 110 whose primary function is traffic management (e.g., routers, access points, and gateways). In some cases, data may be delivered via provider nodes or devices 110 whose primary function (and, in some scenarios, sole function) is to collect and cache data from downstream devices (referred to herein as “historian nodes”). For example, a leveled system of historian nodes or computing devices may be located throughout the network 100, and each node 110 may periodically deliver or stream cached data to a historian node of a similar level, e.g., using the backbone 105. Downstream historian nodes may deliver or stream cached data to upstream historian nodes, and ultimately the historian nodes that are immediately downstream of the process control big data appliance 102 may deliver or stream respective cached data for storage at the process control big data appliance 102.
If desired, nodes 110 that communicate with each other may perform layered caching using the process control system big data network backbone 105, and at least some of the nodes 110 may communicate cached data to other nodes 110 at a different level using another communication network and/or other protocol, such as HART, WirelessHART, Fieldbus, DeviceNet, WiFi, Ethernet, or other protocols.
Of course, while leveled or layered caching has been discussed with respect to provider nodes 110, the concepts and techniques may apply equally to user interface nodes 112 and/or to other types of nodes 115 of the process control system big data network 100. Still further, a subset of the nodes 108 may perform leveled or layered caching, while another subset of the nodes 108 may cause their cached/collected data to be directly delivered to the process control big data appliance 102 without being cached or temporarily stored at an intermediate node. If desired, historian nodes may cache data from multiple different types of nodes, e.g., from a provider node 110 and from a user interface node 112.
Process Control System Big Data Network Backbone
Returning to
The big data network backbone 105 may support one or more suitable routing protocols, e.g., protocols included in the Internet Protocol (IP) suite (e.g., UDP (User Datagram Protocol), TCP (Transmission Control Protocol), Ethernet, etc.), or other suitable routing protocols. At least some of the nodes 108 may utilize a streaming protocol such as the Stream Control Transmission Protocol (SCTP) to stream cached data from the nodes to the process control big data appliance 102 via the network backbone 105. Typically, each node 108 included in the process data big data network 100 may support at least an application layer (and, for some nodes, additional layers) of the routing protocol(s) supported by the backbone 105. Each node 108 may be uniquely identified within the process control system big data network 100, e.g., by a unique network address.
At least a portion of the process control system big data network 100 may be an ad-hoc network. As such, at least some of the nodes 108 may connect to the network backbone 105 (or to another node of the network 100) in an ad-hoc manner. Typically, each node that requests to join the network 100 must be authenticated; however authentication is discussed in more detail in later sections.
Process Control System Big Data Appliance
Continuing with
The process control system big data storage area 120 may comprise multiple physical data drives or storage entities, such as RAID (Redundant Array of Independent Disks) storage, cloud storage, or any other suitable data storage technology that is suitable for data bank or data center storage. However, the data storage area 120 has the appearance of a single or unitary logical data storage area or entity to the nodes 108 of the network 100. As such, the data storage 120 may be viewed as a centralized big data storage area 120 for the process control big data network 100 or for the process plant 10. In some cases, a single logical centralized data storage area 120 may service multiple process plants (e.g., the process plant 10 and another process plant). For example, a centralized data storage area 120 may service several refineries of an energy company. If desired, the centralized data storage area 120 may be directly connected to the backbone 105, via for example at least one high-bandwidth communication link. Additionally, the centralized data storage area 120 may include an integral firewall.
The structure of the unitary, logical data storage area 120 may support the storage of all process control system related data. For example, each entry, data point, or observation of the data storage entity may include an indication of the identity of the data (e.g., source, device, tag, location, etc.), a content of the data (e.g., measurement, value, etc.), and a timestamp indicating a time at which the data was collected, generated, received or observed. As such, these entries, data points, or observations are referred to herein as “time-series data.” The data may be stored in the data storage area 120 using a common format including a schema that supports scalable storage, streamed data, and low-latency queries, for example.
If desired, the schema may include storing multiple observations in each row, and using a rowkey with a custom hash to filter the data in the row. The hash may be based on the timestamp and a tag. For example, the hash may be a rounded value of the timestamp, and the tag may correspond to an event or an entity of or related to the process control system. Additionally, the data storage area 120 may also store metadata corresponding to each row or to a group of rows, either integrally with the time-series data or separately from the time-series data. For example, the metadata may be stored in a schema-less manner separately from the time-series data.
The schema used for storing data at the appliance data storage 120 may also be utilized for storing data in the cache MX of at least one of the nodes 108. Accordingly, the schema may be maintained when data is transmitted from the local storage areas MX of the nodes 108 across the backbone 105 to the process control system big data appliance data storage 120.
In addition to the data storage 120, the process control system big data appliance 102 may further include one or more appliance data receivers 122, each of which is configured to receive data packets from the backbone 105, process the data packets to retrieve the substantive data and timestamp carried therein, and store the substantive data and timestamp in the data storage area 120. The appliance data receivers 122 may reside on a plurality of computing devices or switches, for example. Multiple appliance data receivers 122 (and/or multiple instances of at least one data receiver 122) may operate in parallel on multiple data packets.
If the received data packets include the schema utilized by the process control big data appliance data storage area 120, the appliance data receivers 122 may populate additional entries or observations of the data storage area 120 with the schematic information (and, may optionally store corresponding metadata, if desired). In contrast, if the received data packets do not include the schema utilized by the process control big data appliance data storage area 120, the appliance data receivers 122 may decode the packets and populate time-series data observations or data points of the process control big data appliance data storage area 120 (and, optionally corresponding metadata) accordingly.
Additionally, the process control system big data appliance 102 may include one or more appliance request servicers 125, each of which is configured to access time-series data and/or metadata stored in the process control system big data appliance storage 120, e.g., per the request of a requesting entity or application. The appliance request servicers 125 may reside on a plurality of computing devices or switches, for example. At least some of the appliance request servicers 125 and the appliance data receivers 122 may reside on the same computing device or devices (e.g., on an integral device), or are included in an integral application.
Multiple appliance request servicers 125 (and/or multiple instances of at least one appliance request servicer 125) may operate in parallel on multiple requests from multiple requesting entities or applications. As such, a single appliance request servicer 125 may service multiple requests, such as multiple requests from a single entity or application, or multiple requests from different instances of an application.
As illustrated in
Each node 11, 12, 35 and 78 may transmit at least some of the cached data to one or more appliance data receivers 122a-122c (e.g., using the network backbone 105). For example, at least one node 11, 12, 35, 78 may push at least some of the data from its respective memory MX when the cache is filled to a particular threshold. The threshold of the cache may be adjustable, and at least one node 11, 12, 35, 78 may push at least some of data from its respective memory MX when a resource (e.g., a bandwidth of the network 105, the processor PMCX, or some other resource) is sufficiently available. An availability threshold of a particular resource may be adjustable.
Moreover, at least one node 11, 12, 35, 78 may push at least some of the data stored in the memories MX at periodic intervals. The periodicity of a particular time interval at which data is pushed may be based on a type of the data, the type of pushing node, the location of the pushing node, and/or other criteria. The periodicity of a particular time interval may be adjustable, and at least one node 11, 12, 35, 78 may provide data in response to a request (e.g., from the process control big data appliance 102).
At least one of the nodes 11, 12, 35, 78 may stream at least some of the data in real-time as the data is generated, received or otherwise observed by each node 11, 12, 35, 78 (e.g., the node may not temporarily store or cache the data, or may store the data for only as long as it takes the node to process the data for streaming). For example, at least one of the nodes 11, 12, 35, 78 may stream at least some of the data to the one or more appliance data receivers 122 using a streaming protocol. Hence, a node 11, 12, 35, 78 may host a streaming service, and at least one of the data receivers 122 and/or the data storage area 120 may subscribe to the streaming service.
Accordingly, transmitted data may be received by one or more appliance data receivers 122a-122c, e.g., via the network backbone 105. A particular appliance data receiver 122 may be designated to receive data from one or more particular nodes, or a particular appliance data receiver 122 may be designated to receive data from only one or more particular types of devices (e.g., controllers, routers, or user interface devices). Further, a particular appliance data receiver 122 may be designated to receive only one or more particular types of data (e.g., network management data only or security-related data only).
The appliance data receivers 122a-122c may cause the big data appliance storage area 120 to store or historize the data. For example, the data storage area 120 may store the data received by each of the appliance data receivers 122a-122c using the process control big data schema. As illustrated in
The data storage area 120 may integrate data that is received via the plurality of appliance data receivers 122a-122c so that data from multiple sources may be combined (e.g., into a same group of rows of the data storage area 120). Data that is received via the plurality of appliance data receivers 122a-122c may be cleaned to remove noise and inconsistent data. An appliance data receiver 122 may perform data cleaning and/or data integration on at least some of the received data before the received data is stored, and/or the process control system big data appliance 102 may clean some or all of the received data after the received data has been stored in the storage area 102. A device or node 110, 112, 115 may cause additional data related to the data contents to be transmitted, and the appliance data receiver 122 and/or the big data appliance storage area 120 may utilize this additional data to perform data cleaning. A node 110, 112, 115 may clean (at least partially) at least some data prior to the node 110, 112, 115 causing the data to be transmitted to the big data appliance storage area 120 for storage.
At least some of the appliance request servicers 125 may each provide a particular service or application that requires access to at least some of the data stored in the process control big data storage area 120. For example, the appliance request servicer 125a may be a data analysis support service, and the appliance request servicer 125b may be a data trend support service. Other examples of services 125 that may be provided by the process control system big data appliance 102 may include a configuration application service 125c, a diagnostic application service 125d, and an advanced control application service 125e. An advanced control application service 125e may include, for example, model predictive control, batch data analytics, continuous data analytics or other applications that require historized data for model building and other purposes. The process control system big data appliance 102 may include other request servicers 125 to support other services or applications, e.g., a communication service, an administration service, an equipment management service, a planning service, and other services.
A data requester 130 may be an application that requests access to data that is stored in the process control system big data appliance storage area 120. Based on a request of the data requester 130, the corresponding data may be retrieved from the process control big data storage area 120, and may be transformed and/or consolidated into data forms that are usable by the requester 130. One or more appliance request servicers 125 may perform data retrieval and/or data transformation on at least some of the requested data. The big data appliance 102 further supports casual data access, such as via a user requesting data access casually and repeatedly with variances. In particular, the big data appliance 102 may support privileged APIs that enable more granular and versatile access to the process control big data storage area 120.
At least some of the data requesters 130 and/or at least some of the request servicers 125 may be web services or web applications that are hosted by the process control system big data appliance 102 and that are accessible by nodes of the process control system big data network 100 (e.g., user interface devices 112 or provider devices 110). Accordingly, at least some of the devices or nodes 108 may include a respective web server to support a web browser, web client interface, or plug-in corresponding to a data requester 130 or to a request servicer 125. For user interface devices 112 in particular, a data requester 130 or a request servicer 125 may pull displays and stored data through a User Interface (UI) service layer 135. The UI service layer 135 includes a data visualization service 136 that facilitates the display of various process control data. In particular, the data visualization service 136 may represent various portions of process control data in pictures, charts, maps, reports, presentations, and/or the like. Accordingly, a user accessing any of the data visualization channels may be able to quickly ascertain certain data, trends, relationships, or conclusions associated with the process control data. The data visualization service 136 supports dynamic updating whereby the data visualization service 136 may update corresponding charts or visualizations based on user input, added or removed data, and/or other factors.
A data analysis engine 132 may be an application that performs a computational analysis on at least some of the time-series data points stored in the appliance storage area 120 to generate knowledge or observations. As such, a data analysis engine 132 may generate a new set of data points or observations. The new knowledge, new observations, or new data points may provide a posteriori analysis of aspects of the process plant 10 (e.g., diagnostics or trouble shooting), and/or may provide a priori predictions (e.g., prognostics) corresponding to the process plant 10. In one case, a data analysis engine 132 may perform data mining on a selected subset of the stored data 120, and may perform pattern evaluation on the mined data to generate the new knowledge or new set of data points or observations. Of course, multiple data analysis engines 132 or instances thereof may cooperate to generate the new knowledge or new set of data points.
The new knowledge or set of data points may be stored in (e.g., added to) the appliance storage area 120, for example, and may additionally or alternatively be presented at one or more user interface devices 112. The new knowledge may also be incorporated into one or more control strategies operating in the process plant 10, if desired. A particular data analysis engine 132 may be executed when indicated by a user (e.g., via a user interface device 112), and/or the particular data analysis engine 132 may be executed automatically by the process control system big data appliance 102.
Generally, the data analysis engines 132 of the process control system big data appliance 102 may operate on the stored data to determine time-based relationships between various entities and providers within and external to the process plant 10, and may utilize the determined time-based relationship to control one or more processes of the plant 10 accordingly. As such, the process control system big data appliance 102 allows for one or more processes to be coordinated with other processes and/or to be adjusted over time in response to changing conditions and factors. The process control system big data appliance 102 may automatically determine and execute the coordination and/or adjustments as conditions and events occur, thus greatly increasing efficiencies and optimizing productivity over known prior art control systems.
Examples of possible scenarios in which the knowledge discovery techniques of data analysis engines 132 abound. In one example scenario, a certain combination of events leads to poor product quality when the product is eventually generated at a later time (e.g., several hours after the occurrence of the combination of events). The operator is usually ignorant of the relationship between the occurrence of the events and the product quality. Rather than detecting and determining the poor product quality several hours hence and trouble-shooting to determine the root causes of the poor product quality (as is currently done in known process control systems), the process control system big data appliance 102 (and, in particular, one or more of the data analysis engines 132 therein) may automatically detect the combination of events at or shortly after their occurrence, e.g., when the data corresponding to the events' occurrences is transmitted to the appliance 102. The data analysis engines 132 may predict the poor product quality based on the occurrence of these events, may alert an operator to the prediction, and/or may automatically adjust or change one or more parameters or processes in real-time to mitigate the effects of the combination of events. For example, a data analysis engine 132 may determine a revised set point or revised parameter values and cause the revised values to be used by provider devices 110 of the process plant 10. In this manner, the process control system big data appliance 102 allows problems to be discovered and potentially mitigated much more quickly and efficiently as compared to currently known process control systems.
In another example scenario, at least some of the data analysis engines 132 may be utilized to detect changes in product operation. For instance, the data analysis engines 132 may detect changes in certain communication rates, and/or from changes or patterns of parameter values received from a sensor or from multiple sensors over time which may indicate that system dynamics may be changing. In yet another example scenario, the data analysis engines 132 may be utilized to diagnose and determine that a particular batch of valves or other supplier equipment are faulty based on the behavior of processes and the occurrences of alarms related to the particular batch across the plant 10 and across time.
In another example scenario, at least some of the data analysis engines 132 may predict product capabilities, such as vaccine potency. In yet another example scenario, the data analysis engines 132 may monitor and detect potential security issues associated with the process plant 10, such as increases in log-in patterns, retries, and their respective locations. In still another example scenario, the data analysis engines 132 may analyze data aggregated or stored across the process plant 10 and one or more other process plants. In this manner, the process control system big data appliance 102 allows a company that owns or operates multiple process plants to glean diagnostic and/or prognostic information on a region, an industry, or a company-wide basis.
Big Data Schema for Process Control Data
The big data appliance 102 is configured to use non-relational database mechanisms of a big data schema to store process control data. The non-relational database mechanisms enable design simplicity, horizontal scaling, and finer control over data availability. Generally, the non-relational structure of the big data schema leverages one or more tables to store process control data received from various control system components or modules. The structure of the big data schema enables efficient storage as a result of the tables only storing actual measurements or values (i.e., the tables do not have empty cells), thereby reducing the amount of total storage required by the tables. Further, the organization of the tables enables the use of multiple types of queries to efficiently locate and access stored data.
Generally, each table includes one or more rowkeys, column families, and column qualifiers. Each rowkey serves as a primary key for the corresponding table. The big data appliance 102 examines one or more fields of a received process variable to determine the rowkey to which the process variable should be associated. A column family groups one or more related columns that specify how the process variable should be associated with the rowkey. In particular, each column of a column family specifies one or more column qualifiers corresponding to fields or attributes of the received process variables. An administrator or user may specify the various column families and the column qualifiers thereof. The column qualifiers have one or more values that can result in multiple entries for the same rowkey, therefore resulting in a three-dimensional storage scheme. In some cases, the column qualifiers can correspond to fields or attributes already included in the received process control data. In other cases, the big data appliance 102 can determine or identify the column qualifiers upon receipt of the process control data.
The rowkeys and the column qualifiers can include one or more fields or attributes, or combinations thereof, of the process control data, such as one or more of a timestamp (or a portion thereof), an identification of a process variable, the measurement or value of the process control data, a type of data (e.g., Boolean, integer, etc.), a status of the process variable (e.g., “good,” “bad,” “absent,” etc.), and/or others. For example, each rowkey of a table can be a concatenation of an identification of a process variable and a portion of the timestamp corresponding to when the process variable was recorded, and the column qualifiers of the table can be a concatenation of the type of the process variable, the status of the process variable, and an additional portion of the timestamp, whereby the table stores the measurement value of the process variable in the appropriate data field. For further example, each rowkey of a table can be a concatenation of the type of the process variable and the measurement value of the process variable, and the column qualifiers can be a concatenation of the identification of the process variable and the timestamp, whereby the table stores the status of the process variable in the appropriate data field.
Generally, the big data appliance 102 collects many types of data (e.g., continuous, batch, event, operator-entered values, etc.) from a process control plant and from other sources such as lab systems and material handling systems. For example, the big data appliance 102 collects data such as process variable values, setpoints, discrete inputs and outputs, process alarms, maintenance alarms, operator actions, batch actions, end of batch data, insight models and statistics, and/or the like. The big data appliance 102 automatically buffers the collected data in local memory or storage without requiring any user input or configuration, and periodically transfers the data to a real-time database. Because the big data appliance 102 collects data at the rate at which the associated module of the process control plant is executed, the big data appliance 102 enables a complete history of the process control plant operation to be available to support various analyses.
The big data appliance 102 further leverages a time series database server (TSDS) of the data storage area 120 to store, index, and serve process control data and other related data collected from various control system components (e.g., control strategies, control system equipment, devices, lab systems, applications, etc.) at a large scale and to enable effective retrieval of the data. The TSDS is able to serve up data for traditional applications such as operational historians, and to collect and serve up infrastructure data related to the process control devices and equipment.
The time-series data may be thought of as a collection of data points or tuples, whereby each data point can have a timestamp and a measurement. The TSDS may collect the measurements at regular or irregular intervals, for example at the execution rate of the associated control module. For instance, the TSDS may collect a process variable and an associated status for all analog input points. In some cases, the data points can include metadata indicating the measurement, such as the fully-qualified tag generating the time series, the range on the data, and other data. By appending a timestamp to a value or to a measurement and its status, patterns and differences between and among measurement values over time can be better ascertained. For example, if a current temperature at a specific location is measured every hour, future temperatures can be more easily predicted based on one or more of the measured temperatures. Further, by maintaining the timestamp, location, and measurement information as part of a control hierarchy, the TSDS may store these relationships in the database as metadata and update the relationships as the hierarchy is updated.
As discussed herein, the big data appliance 102 implements the big data storage schema using one or more tables.
The big data appliance 102 examines received process control data to determine how to store the data. In some cases, the big data appliance 102 examines the process control data to identify attributes corresponding to the rowkey (or a portion thereof), or to one or more column qualifiers. For example, the process control data can include an identification of the process variable and a status of the process variable. In other cases, the big data appliance 102 appends data to the received process control data, wherein the appended data corresponds to the rowkey (or a portion thereof), or to one or more column qualifiers. For example, the big data appliance 102 may generate a timestamp corresponding to when the big data appliance 102 received the process control data. Accordingly, the big data appliance 102 may build the rowkeys and/or column qualifiers using the identified or generated attributes of the process control data. Further, the big data appliance 102 may store the process control data (or measurements or values thereof) according to the built rowkeys and column qualifiers.
Generally, the big data appliance 102 does not allocate memory segments of the table 500 prior to storing data in the table 500. Instead, the big data appliance 102 is configured to store data associated with the process variable (e.g., the measurement or value) in the table 500 according to the corresponding rowkey, column family, and column qualifiers. Further, the big data appliance 102 stores the data as the process control data is received and processed. For example, the big data appliance 102 stores data 509 in a memory segment corresponding to the first rowkey 505, the first column family 506, and CQ1 of a certain value; and the big data appliance 102 stores data 511 in a memory segment corresponding to the second rowkey 501, the second column family 507, and CQ5 of a certain value. The remaining fields of the table are null or otherwise unallocated, thus conserving memory space in the big data schema.
In an example implementation, the big data appliance 102 orders the rowkeys according to a timestamp of the corresponding process control data and process variables thereof. The timestamp may correspond to when a device recorded the process control data, when the device transmitted the process control data, when the big data appliance 102 received the process control data, or other times. Further, the big data appliance 102 may round the timestamp down or up by a predetermined degree. For example, timestamp may be rounded down or up to the nearest minute, hour, day, or the like. As a result, the big data appliance 102 may store multiple columns having timestamps that are included in the rounded timestamp of the corresponding rowkey. By storing multiple columns per rowkey, searching the process control system big data storage 120 is more efficient and effective. In particular, this structure enables more data to be disqualified in a single exclusion and the overall number of rows that are tracked by rowkey to be reduced. Further, by using the rounded time as a part of the hash, an administrator may partition the big data schema more effectively.
Generally, to write a measurement, the big data appliance 102 builds the appropriate rowkey with unique data and determines the associated column family and column qualifier(s). Further, the big data appliance 102 identifies which bytes to store in the cell corresponding to the column family and column qualifier(s), and writes the associated record. For example, the table 531 writes the values of the process variables in the associated records. A user or administrator associated with the process control system 10 may query the big data appliance 102 for stored data. In particular, the query may specify a rowkey range as well as define applicable filter criteria, such as an upper timestamp range. The big data appliance 102 may execute the query and return identified results to the querying user.
The big data appliance 102 can employ “snapshot” or “zoom” features to enable users to gauge a long-term view and overall context of the data while still enabling more detailed views of the data. The snapshot or zoom features further enable users to identify snapshots of data corresponding to various timestamp ranges. Using various techniques, the big data appliance 102 can create and store aggregates of the data for specific time periods. For example, the big data appliance 102 can store the minimum, maximum, and closing measurement values for each hour of data (i.e., from measurement values corresponding to a common upper timestamp).
The big data appliance 102 may support Apache Hadoop for storage and large scale processing of the associated data. In some cases, the big data appliance 102 may implement the MapReduce framework associated with the Apache HBase database, which enables users or administrators to reduce data while at the same time enabling the HBase infrastructure to utilize parallel distributed programs. The MapReduce framework enables the division of a dataset and to run it in parallel over multiple nodes. Specifically, the users or administrators may divide the storage problem into simpler Map( ) and Reduce ( )functions for filtering, sorting, and summary operations, while the MapReduce framework automatically marshalls the distributed servers, runs the various tasks in parallel, manages communications and data transfers between the various parts of the system, provides for redundancy and failures, and manages the overall process.
A second interface 685 depicts charts resulting from the selections of the first interface 680. In particular, the second interface 685 depicts hourly and monthly charts 686, 687 for process variable “PV008,” and hourly and monthly charts 688, 689 for process variable “PV059.” The respective hourly charts 686, 688 depict hourly minimum, maximum, and average values for “PV008” and “PV059” over a period of six (6) hours. The respective monthly charts 687, 689 depict monthly minimum, maximum, and average values for “PV008” and “PV059” over a period of five (5) months. Using the first interface 680 and the second interface 685, the user or administrator may effectively and efficiently assess and analyze snapshots of process control data and parameters thereof without having to filter through all of the recorded data.
Referring to
At a block 810, the big data appliance identifies various data measurements, indications, and other attributes from each respective portion of the received data. For example, the big data appliance may identify, for each respective portion of the data, an identification of a respective process variable, a timestamp associated with the respective portion of the data, a measurement value associated with the respective process variable, a data type of the measurement, and/or a status associated with the measurement value. The timestamp may be represented as a UNIX epoch value, and may include a first timestamp portion reflecting the timestamp rounded down by a certain degree, and a second timestamp portion reflecting a remainder of the timestamp that was rounded down by the certain degree. Accordingly, the first timestamp portion may correspond to an upper timestamp of the timestamp and the second timestamp portion may correspond to a lower timestamp of the timestamp.
At block 815, the big data appliance identifies, for each respective portion of the received data, a rowkey based on the respective process variable and the first timestamp portion. In some cases, the big data appliance may generate the rowkey (e.g., if the particular rowkey does not exist) by concatenating an identification of the respective process variable and the first timestamp portion, and store the rowkey in a data storage device. In other cases, the big data appliance may identify a rowkey already stored in a data storage device that corresponds to the respective process variable and the first timestamp portion.
At block 820, the big data appliance stores, for each respective portion of the data, various data within a portion of the data storage device associated with the rowkey. In some cases, the big data appliance may store the second timestamp portion, the measurement value, optionally the type of the measurement value, and optionally the status of the measurement value. In this regard, the rowkey can include recorded process control data having a timestamp corresponding to the upper timestamp of the rowkey (i.e., data recorded within a period of time indicated by the rowkey), thus reducing the amount of storage necessary to store the process control data and reducing the amount of time needed to access and retrieve the data.
At block 825, the big data appliance determines if additional process control data is received. For example, the additional process control data can be additional process control data recorded by the process control system. If additional process control data is received (“YES”), processing can return to 810 or proceed to any other functionality. If additional process control data is not received (“NO”), processing can end, repeat, or proceed to any other functionality.
Referring to
At a block 910, the big data appliance, for each record, examines the data to identify (1) a respective process variable, (2) a measurement value associated with the respective process variable, and (3) a timestamp including a first timestamp portion and second timestamp portion. The timestamp may be represented as a UNIX epoch value, and may include a first timestamp portion reflecting the timestamp rounded down by a certain degree, and a second timestamp portion reflecting a remainder of the timestamp that was rounded down by the certain degree. Accordingly, the first timestamp portion may correspond to an upper timestamp of the timestamp and the second timestamp portion may correspond to a lower timestamp of the timestamp.
At a block 915, the big data appliance determines that a set period of time based on the first timestamp portion has elapsed. For example, if the first timestamp portion specifies 10:00:00 AM and is rounded to the nearest hour, the set period of time elapses at 11:00:00 AM. In one case, the big data appliance can determine that the set period of time has elapsed by comparing a current time to first timestamp portion.
When the set period of time has elapsed at block 920, the big data appliance identifies at least one statistical parameter from one or more of the plurality of records having a timestamp within the set period of time. The at least one statistical parameter may be one or more of: a high value of the respective one or more measurement values, a low value of the respective one or more measurement values, a most recent value of the respective one or more measurement values, a standard deviation of the respective one or more measurement values, an average of the respective one or more measurement values, and a median of the respective one or more measurement values. It should be appreciated that other statistical parameters associated with the plurality of records are envisioned.
At block 925, the big data appliance filters the at least one statistical parameter according to the respective process variable. In particular, the big data appliance can separate the identified statistical parameter(s) according to the respective process variable such that a user can access or retrieve statistical parameter data according to the specified process variable. At block 930, the big data appliance stores the at least one statistical parameter in a time period data record associated with the set period of time and the respective process variable. Therefore, the time period data record can store any relevant data corresponding to a specific time period and a user need not review or access individual rowkeys or records to analyze aggregate process control data.
Referring to
At block 1010, the big data appliance retrieves the portion of the process control data corresponding to the set period of time wherein the portion of the process control data includes a plurality of records. The set period of time may be defined by an upper timestamp associated with the plurality of records, wherein the plurality of records each indicate a lower timestamp that falls within the set period of time defined by the upper timestamp.
At block 1015, the big data appliance, for each of the plurality of records, identifies (1) a respective process variable, (2) a measurement value associated with the respective process variable, and (3) a timestamp that falls within the set period of time. The timestamp may be represented as a UNIX epoch value, and may include a first timestamp portion reflecting the timestamp rounded down by a certain degree, and a second timestamp portion reflecting a remainder of the timestamp that was rounded down by the certain degree. Accordingly, the first timestamp portion may correspond to an upper timestamp of the timestamp and the second timestamp portion may correspond to a lower timestamp of the timestamp.
At block 1020, the big data appliance generates aggregate process control data from the plurality of records. The big data appliance may aggregate the plurality of records according to at least one statistical parameter associated with each of the plurality of records. For example, the at least one statistical parameter may be one or more of: a high value of the respective one or more measurement values, a low value of the respective one or more measurement values, a most recent value of the respective one or more measurement values, a standard deviation of the respective one or more measurement values, an average of the respective one or more measurement values, and a median of the respective one or more measurement values. The big data appliance may also calculate the at least one statistical parameter from the plurality of records, such as in cases in which the at least one statistical parameter is not explicitly indicated in the aggregate process control data. For example, the big data appliance may calculate averages, standard deviations, high values, low values and/or other metrics to generate the aggregate process control data. In some cases, a user may specify a desired statistical parameter, for example as part of a request or command, whereby the big data appliance calculates the appropriate statistical parameter from the plurality of records.
At block 1025, the big data appliance presents the aggregate process control data to the user. For example, the big data appliance may present the aggregate process control data as numeric data, a chart, a graph, or any other type of numeric data or graphical indication. Further, the aggregate process control data may indicate any statistical parameters that are identified or calculated from the plurality of records.
At block 1030, the big data appliance determines if an additional request is received. For example, the user may wish to narrow or expand the aggregated process control data, or may wish to perform other calculations on the process control data. If the additional request is received (“YES”), processing can return to 1010 or proceed to any other functionality. If the additional request is not received (“NO”), processing can end, repeat, or proceed to any other functionality.
When implemented in software, any of the applications, services, and engines described herein may be stored in any tangible, non-transitory computer readable memory such as on a magnetic disk, a laser disk, solid state memory device, molecular memory storage device, or other storage medium, in a RAM or ROM of a computer or processor, etc. Although the example systems disclosed herein are disclosed as including, among other components, software and/or firmware executed on hardware, it should be noted that such systems are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of these hardware, software, and firmware components could be embodied exclusively in hardware, exclusively in software, or in any combination of hardware and software. Accordingly, while the example systems described herein are described as being implemented in software executed on a processor of one or more computer devices, persons of ordinary skill in the art will readily appreciate that the examples provided are not the only way to implement such systems.
Thus, while the present invention has been described with reference to specific examples, which are intended to be illustrative only and not to be limiting of the invention, it will be apparent to those of ordinary skill in the art that changes, additions or deletions may be made to the disclosed embodiments without departing from the spirit and scope of the invention.
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