Method and system for high performance integration, processing and searching of structured and unstructured data

Description

FIELD OF THE INVENTION

The present invention is generally directed toward the fields of metadata generation, data indexing, and database acceleration. Various embodiments are directed toward indexing high speed data streams. Additional various embodiment are directed toward enterprise data warehouse acceleration, including areas such as enterprise data searching, document warehousing, text mining, text analytics, information access and enabling actionable intelligence.

Terminology

The following paragraphs provide several definitions for various terms used herein. These paragraphs also provide background information relating to these terms.

GPP: As used herein, the term “general-purpose processor” (or GPP) refers to a hardware device having a fixed form and whose functionality is variable, wherein this variable functionality is defined by fetching instructions and executing those instructions (for example, an Intel Xeon processor or an AMD Opteron processor), of which a conventional central processing unit (CPU) is a common example.

Reconfigurable Logic: As used herein, the term “reconfigurable logic” refers to any logic technology whose form and function can be significantly altered (i.e., reconfigured) in the field post-manufacture. This is to be contrasted with a GPP, whose function can change post-manufacture, but whose form is fixed at manufacture.

Software: As used herein, the term “software” refers to data processing functionality that is deployed on a GPP or other processing devices, wherein software cannot be used to change or define the form of the device on which it is loaded.

Firmware: As used herein, the term “firmware” refers to data processing functionality that is deployed on reconfigurable logic or other processing devices, wherein firmware may be used to change or define the form of the device on which it is loaded.

Coprocessor: As used herein, the term “coprocessor” refers to a computational engine designed to operate in conjunction with other components in a computational system having a main processor (wherein the main processor itself may comprise multiple processors such as in a multi-core processor architecture). Typically, a coprocessor is optimized to perform a specific set of tasks and is used to offload tasks from a main processor (which is typically a GPP) in order to optimize system performance. The scope of tasks performed by a coprocessor may be fixed or variable, depending on the architecture of coprocessor. Examples of fixed coprocessor architectures include Graphics Processor Units which perform a broad spectrum of tasks and floating point numeric coprocessors which perform a relatively narrow set of tasks. Examples of reconfigurable coprocessor architectures include reconfigurable logic devices such as Field Programmable Gate Arrays (FPGAs) which may be reconfigured to implement a wide variety of fixed or programmable computational engines. The functionality of a coprocessor may be defined via software and/or firmware.

Hardware Acceleration: As used herein, the term “hardware acceleration” refers to the use of software and/or firmware implemented on a coprocessor for offloading one or more processing tasks from a main processor to decrease processing latency for those tasks relative to the main processor.

Enterprise: As used herein, the term “enterprise” refers to any business organization or governmental entity that stores and/or processes data (referred to as “enterprise data”) as part of its ongoing operations.

Database: As used herein, the term “database” refers to a persistent data store with indexing capabilities to expedite query processing. Various database management system (DBMS) implementations might be categorized as relational (RDBMS), object-oriented (OODBMS), hierarchical, etc.; however, the dominant architecture in today's industry is a relational, row-column, structured query language (SQL)-capable database. An ANSI-standard SQL database engine is a mature software architecture that can retrieve structured data in response to a query, usually in an efficient manner.

Structured Data: As used herein, the term “structured data” refers to data that has been normalized and persisted to a relational database. Normalization is the data design process of putting data into a tabular, row-column format and abstracting duplicate data into separate tables. Structured data in relational columns is capable of being indexed with B-tree indexes, significantly speeding access to the data in these columns. In SQL terms, structured columns have size limits. These columns may have constraints and referential integrity applied to them in order to ensure consistent data quality. Examples of common structured SQL datatypes are: INT(eger), NUMBER, CHAR(acter), VARCHAR, DATE, TIMESTAMP. The processing of structured data is what known relational database technology is well-suited for. Very importantly, various embodiments of the present invention leverage these capabilities to do what relational databases do best: quickly access structured data using indexed lookups.

Unstructured Data: As used herein, the term “unstructured data” refers to data that falls outside the scope of the definition above for structured data. Thus, the term unstructured data encompasses files, documents or objects with free form text or embedded values included therein. This data includes the complete set of bytes, often including binary-format data, that was used by the application that generated it. Examples of unstructured data include word processing documents (e.g., Microsoft Word documents in their native format), Adobe Acrobat documents, emails, image files, video files, audio files, and other files in their native formats relative to the software application that created them. In SQL terms, unstructured columns have very large, if not unlimited size. Common examples of unstructured SQL datatypes are: BLOB, TEXT, XML, RAW, and IMAGE. Unstructured objects may also be stored outside the database, for example in operating system files. Access to these external objects from within the database engine uses links in the metadata in the database table to the storage location.

There are a number of reasons why XML is will not normally be categorized as “structured” as that term is used herein:

- XML may have large or unlimited sized values
- XML often does not have strongly enforced datatyping
- XML has a flexible schema
- XML values in elements and attributes is often not as rigidly conformed and carefully cleansed as traditional “structured” database columns
  
  Although the concept of “semi-structured” data with flexible schemas is emerging, particularly for XML, for present purposes everything that is not has not been normalized and persisted to a relational database will be considered unstructured data. As such, a column that is of the XML datatype would thus fall under this present definition of “unstructured data”. XML data is a prime candidate for hardware-accelerated searching and integration as outlined in for the present invention.
  
  Metadata: As used herein, the term “metadata” in the context of data objects and documents refers to data that describes or characterizes a data object or document. Examples of object and document metadata include but are not limited to file type, byte size, date created, date last modified, author, title, information about the document's/object's data source (optionally including the name and version number of the program used to generate the document), information about whether the data matches other data, subject coverage, classification information (e.g. information about its concepts, person/place/entity names that are found within the document/data object, word counts, etc.), position information relating to data within a document/object, or other content-derived information about documents/objects.
  
  Bus: As used herein, the term “bus” refers to a logical bus which encompasses any physical interconnect for which devices and locations are accessed by an address. Examples of buses that could be used in the practice of the present invention include, but are not limited to the PCI family of buses (e.g., PCI-X and PCI-Express) and HyperTransport buses.
  
  Pipelining: As used herein, the terms “pipeline”, “pipelined sequence”, or “chain” refer to an arrangement of application modules wherein the output of one application module is connected to the input of the next application module in the sequence. This pipelining arrangement allows each application module to independently operate on any data it receives during a given clock cycle and then pass its output to the next downstream application module in the sequence during another clock cycle.
  
  Full-Text Search: As used herein, the term “full-text search” refers to scanning through the entire body of a document or object, considering every word or byte. This processing may allow for approximations, tokenization based on tagging for a flexible schema, wild-carding, or complex matching.
  
  SQL-Enabled Client Application: As used herein, the term “SQL-enabled” in the context of a client application refers to a client application that is capable of accessing a relational SQL-based database server. The ANSI-standard SQL language has enabled a large number of advanced software client applications to evolve that all are capable of accessing a relational SQL-based database server. Examples of these SQL-enabled client applications include: Business Intelligence (BI) Reporting Tools, Extract Transfer Load (ETL) Tools, enterprise software applications (ERP, CRM, SAP), middleware, plus any of a large number of custom-built applications in a variety of programming languages.
  
  Business Intelligence Reporting Tool: As used herein, the term “business intelligence reporting tool” (or “BI reporting tool”) refers to a software application that provides user-friendly graphical interfaces (GUIs) for formulating search queries into relational databases and generating and presenting reports. A BI reporting tool translates a user-specified graphically-prepared query into a standardized database query such as a SQL command. The so-formulated SQL command is then delivered to an RDBMS to effectuate retrieval of the desired data.
  
  Text Analytics and Text Mining: As used herein, the terms “text analytics” and “text mining” refer to algorithms that operate on document objects using complex language concepts such as semantics. Examples of text analytic/text mining processing include: named entity recognition, content extraction, document classification, document summarization, natural language processing, statistical pattern learning, and relevance ranking.

BACKGROUND OF THE INVENTION

Enterprises continue to store and manage their data in a variety of disparate manners. One manner by which enterprises store their data is within relational databases using relational database management systems (RDBMSs). The tabular, normalized data stored in such RDBMSs is commonly referred to as structured data. For example, an enterprise may format, cleanse, conform, and store its sales records and customer information as structured data within an RDBMS. A variety of well-known tools have been developed in the art for intelligently accessing such structured data, typically based on standardized data languages such as the Structured Query Language (SQL).

However, it is commonly estimated that such tabular structured data represents only a tiny fraction of the totality of an enterprise's stored data. The remainder of the stored data is typically comprised of unstructured data whose storage is usually spread out amongst a variety of different file systems and storage means within the enterprise. An explosion of unstructured objects and documents has left many enterprises with a serious case of “information overload”. Intelligent and unified access to all of this structured and unstructured data has posed a difficult challenge. Contributing to this difficulty is the fact that, with many enterprises, storage of an enterprise's unstructured data is managed separately from the databases, often by different organizational units. A huge challenge that many organizations face is to efficiently and effectively integrate their structured data in relational databases with the rest of this relatively unorganized mass of other unstructured data including blobs. Structured data can provide answers to relatively straight-forward questions like “what?”, “where?”, “when?”, “who?”; by using text analytics, unstructured data can answer more complex questions like “why?”.

FIG. 1(a) illustrates this problem. With many enterprises, there is very little organization as to where all of the documents are located amongst a number of different servers spread throughout the enterprise. For example, the storage space 102 within which an enterprise stores its data may be spread amongst separate components such as a Document Management System A 104, a Network File Server B 106, and an Application Server C 108. To gain access and locate desired documents within this storage space, a user 100 will likely be forced to use different tools to access each of the different components (e.g., using a custom application to access system 104, using a software product such as Windows Explorer to access server 106, and using a custom Application Programming Interface (API) to access server C). To conduct a search for data on the Internet 110, still another tool would likely be used (e.g., a web search tool such as Google). With such a jumble of document locations and access means, the user must not only be knowledgeable as to where within the storage space 102 the documents of interest are located but also proficient in working with a number of different tools for accessing the disparate components 104, 106 and 108. Further still, through enterprise search capabilities like the one depicted in FIG. 1(a), the user does not have the ability to directly access and correlate his or her searches with other enterprise data that is stored in relational databases.

When a user's search includes some form of full-text search, the software that supports such full-text querying will often take a relatively long time to complete, particularly when the query requires scanning the entire bodies of many large documents. This slowness is due, in part, to inherent constraints on the performance of general purpose processors (GPPs) when executing traditional software. Current indexing techniques have important limitations to yielding “find-ability”. Although indexing can be somewhat helpful in locating relevant documents, the task of searching for mis-spellings, alternate spelling variations, regular expressions, or searching for a large number of terms are problems not easily or quickly solved with current indexing solutions, and the time to create an effective index often becomes intractable. To state it differently, in order to build an effective index to help find something, it must be known beforehand what one is trying to find. One example of a shortcoming in conventional systems is that there is no easy or standard way to search for mis-spellings. These problems are compounded in situations where the data are dynamic or constantly changing.

Thus, with respect to an enterprise search environment such as that shown in FIG. 1, the inventors believe:

- 1) There is very little organization as to where all of the various documents are located amongst a number of servers in many different departments throughout the organization.
- 2) Search is dependent on the user knowing whether to look for documents. Multiple search interfaces are required.
- 3) The user does not have direct access to correlate other enterprise data in relational databases.
- 4) Queries may take quite a long time to process, especially where full-text search is required on many large documents.

With respect to structured data, SQL has enjoyed widespread deployment within industry because of its ability to provide a standardized, consistent programming interface to many relational databases. However, the inventors herein recognize that current attempts to standardize the integration of SQL for structured data with full-text search capabilities (or other processing capabilities such as text analytics and text mining) on unstructured data have shown a need for improvement. The implementations of these attempts often evidence performance bottlenecks. Several efforts have arisen to extend standard SQL to integrate structured, tabular data and various forms of unstructured data. For example, SQL/XML for relational access to semi-structured XML data, SQL/MM for unstructured multimedia data, SQL/MED for unstructured external data, and XQuery 1.0 and XPath 2.0 Full-Text 1.0 for searching XML data using regular expressions, wildcards, stemming, thesaurus and boolean operations. The inventors herein believe that these SQL extensions' abilities to deal with unstructured data largely represent an inconsistent and mixed jumble of dialects, which has hindered their widespread adoption in the IT industry. In the inventors' opinion, it is likely that serious performance issues have often slowed up these standardization efforts.

The widespread adoption of SQL has also lead to the development of a number of business intelligence (BI) reporting tools. The inventors believe that reporting tools' functionality for supporting unstructured text analysis is relatively limited and that a need exists in the art for improvements in this area. Most of these software tools have relatively modest abilities to perform full-text searches on unstructured data and other advanced text mining and analytics. The inventors reiterate their belief that the tools' performances have not been particularly efficient.

Furthermore, enterprises such as corporations, institutions, agencies, and other entities have massive amounts of data that they need to manage. As noted above, while some of an enterprise's critical data are normalized, structured, and stored in relational databases, most enterprises' data (generally thought to be around 80% of enterprise data) is unstructured. With conventional computing systems, effective management and efficient access to such unstructured data is problematic.

Indexing is a well-known technique that is used to increase the efficiency by which data can be searched. An index is a list of terms and pointers associated with a collection of data. An example of such an index 100 is shown in FIG. 1(b). Index 100 comprises a plurality of index entries 102, with each index entry 102 comprising a term 104 (see the “term” column in the table) and one or more pointers 106 (see the “pointer(s)” column in the table). The terms 104 in an index can be words, phrases, or other information associated with the data. In many situations, these terms are user-specified. Each pointer 106 in an index corresponds to the term 104 for that entry 102 and identifies where that term can be found in the data. With unstructured data, the data collection often comprises a plurality of documents. Examples of documents include items such a word processing files, spreadsheet files, emails, images, Adobe Acrobat files, web pages, books, pages of books, etc.

However, the inventors note their belief that conventional indexing techniques require a tremendous amount of time to generate an effective index. Even relatively small data sets can take days to effectively index with conventional indexing techniques deployed in software on central processors such as GPPs because of indexing's computationally-intensive nature. Because of the sheer volume of data that enterprises encounter on a daily basis, it is simply not practical for enterprises to index all of the data in its possession (and to which it has access) using these conventional indexing techniques. Instead, enterprises are forced to make a priori decisions as to which data will be subjected to indexing; this is particularly true for unstructured data which comprises the bulk of most enterprises' data. In doing so, enterprises are left without an effective means for efficiently managing and searching much of its data.

SUMMARY OF THE INVENTION

Accordingly, the inventors believe that there is a significant need in the art for a system that provides faster and more unified access to unstructured data. Moreover, the inventors herein further believe that there is a need in the art for a better manner by which structured and unstructured data are coordinated and integrated with each other to support intelligent access of unstructured data.

Moreover, in an effort to address the need in the art for improved management and searching of data, the inventors herein disclose a technique for hardware-accelerating the generation of metadata for data. This data may comprise both structured and/or unstructured data. From this metadata, rich indexes can be generated to enable robust and high performance data searching and analysis. With embodiments of the present invention, data is streamed into a coprocessor, and metadata for the indexes is generated at bus bandwidth rates, thereby leading to dramatic improvements in indexing latency. Because of such improvements in latency, much larger amounts (if not all) of an enterprise's data can be efficiently and effectively indexed.

Toward these ends, with respect to an example embodiment, the inventors disclose a new method and system configured to leverage hardware acceleration of the more complex unstructured data analysis during query processing in a manner that is tightly integrated with traditional standards-based query processing of structured data. In doing so, the present invention preferably harnesses the underlying hardware-accelerated technology disclosed in the following patents and patent applications: U.S. Pat. No. 6,711,558 entitled “Associated Database Scanning and Information Retrieval”, U.S. Pat. No. 7,139,743 entitled “Associative Database Scanning and Information Retrieval using FPGA Devices”, U.S. Patent Application Publication 2006/0294059 entitled “Intelligent Data Storage and Processing Using FPGA Devices”, U.S. Patent Application Publication 2007/0067108 entitled “Method and Apparatus for Performing Biosequence Similarity Searching”, U.S. Patent Application Publication 2008/0086274 entitled “Method and Apparatus for Protein Sequence Alignment Using FPGA Devices” (published from U.S. application Ser. No. 11/836,947, filed Aug. 10, 2007), U.S. Patent Application Publication 2007/0130140 entitled “Method and Device for High Performance Regular Expression Pattern Matching”, U.S. Patent Application Publication 2007/0260602 entitled “Method and Apparatus for Approximate Pattern Matching” (published from U.S. application Ser. No. 11/381,214, filed May 2, 2006), U.S. Patent Application Publication 2007/0174841 entitled “Firmware Socket Module for FPGA-Based Pipeline Processing”, and U.S. Patent Application Publication 2007/0237327 entitled “Method and System for High Throughput Blockwise Independent Encryption/Decryption”), the entire disclosures of each of which are incorporated herein by reference.

This hardware acceleration is applied toward those portions of query handling that are well-suited to hardware acceleration (e.g., full-text search operations performed on unstructured data). To intelligently limit which unstructured data should be applied to the hardware-accelerated data processing operation (and thereby speed the overall response time), a system that embodies the present invention may also employ indexed queries of structured data stored in a database. Preferably, these queries are formulated as standardized indexed database queries, such as SQL commands directed toward RDBMSs. In this manner, users can formulate queries targeted toward both structured and unstructured data in a familiar way. An API in accordance with a preferred embodiment of the invention can be employed to effectively bifurcate query processing into a structured data portion and a hardware-accelerated unstructured data portion.

The hardware-accelerated data processing operation is preferably carried out by computer resources other than a GPP (preferably a coprocessor such as a reconfigurable logic device on which firmware is deployed), as described in the above-referenced and incorporated patents and patent applications. By utilizing a coprocessor for this purpose, significant acceleration in query processing is attained relative to conventional solutions that perform full-text searching of unstructured data using traditional software executed by GPPs, thereby freeing the system's GPP(s) to perform other system tasks.

The structured and indexed data that aids the query handling process preferably comprises at least in part the object's (e.g. document's) metadata. This metadata, which is preferably stored in structured relational tables within an RDBMS, can be queried using standardized queries such as SQL commands to identify which subsets of unstructured data should be streamed through the coprocessor. In effect, according to one aspect, the present invention essentially SQL-enables the data processing capabilities of the coprocessor.

Preferably, the unstructured data that has been indexed with metadata is stored within high-performance disk space within an appliance that employs the coprocessor on which the data processing functionality for the unstructured data is deployed. In this manner, the unstructured data can be streamed through the coprocessor without network bandwidth constraints. The unstructured data can also be stored in some location that is accessible to appliance 200 via a high speed network.

The inventors herein further disclose that the generation of metadata from unstructured objects can also be hardware accelerated using a coprocessor (preferably using a coprocessor in the form of a reconfigurable logic device with appropriate firmware deployed thereon). Unstructured objects for which metadata is to be generated can be streamed through the appropriately-configured coprocessor to thereby expedite the generation of metadata that is used to index that unstructured data. Following this metadata generation, the entire bodies of these unstructured objects are preferably ingested into the disk space of the appliance.

The metadata is also preferably stored in an RDBMS internal to the appliance, but it should be noted that structured data stored in other relational databases external to the appliance can also be accessed as part of the query processing functionality of a preferred embodiment of the present invention.

The inventors envision that, in addition to the generalized data search described below, the present invention can be applied toward a myriad of applications. For example, in health care case management, various disparate data sources such as clinical research databases, patient record databases, databases of insurance and regulatory filings, and regulations databases can be integrated through the appliance described herein, thereby enhancing the abilities of health care organizations with respect to enhancing diagnoses, reducing misdiagnoses, ensuring adequate treatment, promoting quality of service, increasing utilization of available resources, reducing fraud, controlling costs and other goals.

In the scientific field, disparate data sources such as scientific and clinical literature, medical treatment records and reports, chemical compound databases, pharmaceutical databases, medical symptom databases and the like can be integrated using the appliance described herein. In this manner, the desirable goals include extracting relationships among biomedical and chemical entities, genetic markers—e.g., proteins and genes, base sequences—and symptoms—patterns such as “A inhibits B”, “A activates B”, and “A is associated with B”. Entity extraction in the context can refer to recognition of gene, protein, chemical, symptom and syndrome names and signatures from biomedical and chemical texts based on domain dictionaries.

In the intelligence and counter-terrorism field, disparate data sources such as news and investigative reports, communications intercepts, documents, and case files (all in a variety of languages) can be integrated through the appliance described herein. The targets and patterns that can be detected through integrated and intelligent access to this data includes organizational associations and networks, behavioral/attack patterns, threat assessment, strategy development, tactical evaluation, and event prediction.

In the law enforcement field, similar data sources to the intelligence/counter-terrorism field can be integrated, along with crime and court reports, legal documents, as well as geographic and demographic data using the appliance described herein. Goals of such integration would include detection of crime patterns (temporal, geospatial, interpersonal, and/or organizational) as well as support of criminal investigations and prosecutions.

In the securities fraud detection field, disparate data sources such as financial and news reports, corporate filings and documents, and trading and other transactional records can all be integrated using the appliance described herein, thereby enhancing the ability to detect such activities as insider trading, reporting irregularities, money laundering, illegal transactions, and pricing anomalies.

In the field of customer relationship management (CRM), disparate data sources such as customer email and letters, call center notes and transcripts, as well as other customer data maintained in existing CRM systems can all be integrated using the appliance described herein. Through such integration, one can potentially identify product and service quality issues, and assist in product design and management.

In the reputation management field, disparate data sources can include news reports, web pages, and market analyses can be integrated using the appliance described herein to perform text mining and pattern detection operations that reveal the state of an enterprise's relations with the public.

Similarly, the appliance described herein can be used as a social network analysis tool that analyzes email and other communications, corporate documents, and news reports to determine the connectedness of individuals and organizations.

Other areas that are believed to be ripe for the deployment of the appliance described herein include operations management, competitive intelligence, legal discovery (e.g., where a plaintiff in a law suit requests all data maintained or under the control of the defendant that relates to “John Smith”), content rights management, regulatory compliance, and others.

Furthermore, the invention described herein can be used to significantly accelerate metadata generation operations that are performed on data, including automated generation of content-derived metadata.

As documents are streamed into a coprocessor, any of a number of metadata generation operations can be flexibly performed on the document stream. For example, exact matching and approximate matching operations can be performed on the words within a document stream to find which words within the document stream are “hits” against the words in any of a number of dictionaries. From these “hits”, rich indexes can quickly be generated.

For ease of reference, the term “documents” will be often be used to describe the unstructured data that is indexed in accordance with various embodiments of the present invention. However, it should be noted that the use of the term “documents” is meant to be only exemplary as other forms unstructured data can also be indexed using the techniques described herein. Also, the term “words” is used to describe bits within a data stream that are grouped together for the purposes of metadata generation operations. Words preferably correspond to the bytes that form discrete character strings, wherein the characters may be any combination of letters, numbers, or other characters.

In accordance with an extremely powerful aspect of an embodiment of the invention, the coprocessor can perform regular expression pattern matching on a received document stream to detect whether any of a plurality of pre-defined patterns exist within the document stream. Examples of patterns that can detected using regular expression pattern matching include social security numbers, credit card numbers, telephone numbers, email address, etc. By developing indexes based on these patterns, an enterprise can effectively answer an indefinite pattern-based question such as “How many of our documents contain a credit card number?” without having to re-analyze all of the enterprise's documents. Instead, the generated index pertaining to the credit card pattern can be accessed to lookup all documents that have been identified as containing a credit card number.

Furthermore, classification and clustering operations can be performed on documents using a coprocessor to further analyze and index those documents.

The high performance stream indexing that is enabled by the present invention has a myriad of useful applications. For example, the email traffic within and/or incoming to an enterprise can be efficiently indexed “on the fly” before or as that email traffic reaches its addressees.

Similarly, the web traffic (e.g., the web pages downloaded by user computers within an enterprise) can also be indexed “on the fly” using various embodiments of the present invention.

Web search engines could employ the embodiments of the present invention to stream web page content through an appliance configured to perform the metadata generation and index generation techniques described herein. The web search engine can then apply search queries against the generated index(es) when formulating responses to those search queries. It is expected that the present invention can be employed to dramatically reduce the lag time that it takes for new web pages to become “findable” through Internet search engines.

Also, the embodiments of the present invention can be employed to operate on data being saved to a disk by any computers within an enterprise.

Another application pertains to analyzing and indexing data streams such as incoming sales transactions, incoming news wire information, and log files.

Yet another application pertains to retroactively indexing existing enterprise data, including indexing previously-unindexed enterprise data and re-indexing previously-indexed enterprise data.

These and other features and advantages of the present invention will be apparent to those having ordinary skill in the art upon review of the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1(a) illustrates a conventional manner by which enterprises provide users with access to their data;

FIG. 1(b) depicts an exemplary index;

FIG. 2 illustrates an exemplary embodiment of the present invention;

FIG. 3 illustrates an exemplary overview of a document ingest pre-processing operation in accordance with an embodiment of the present invention;

FIG. 4 illustrates an exemplary search appliance in accordance with an embodiment of the present invention;

FIG. 5 illustrates an exemplary logical view of a document ingest pre-processing operation in accordance with an embodiment of the present invention;

FIG. 6 illustrates an exemplary data flow within the search appliance of FIG. 4 for a document ingest pre-processing operation in accordance with an embodiment of the present invention;

FIGS. 7(a) and (b) illustrate exemplary printed circuit boards for use in the search appliance of FIG. 4;

FIG. 8 illustrates an example of how a firmware pipeline can be deployed across multiple reconfigurable logic devices;

FIG. 9 illustrates an exemplary overview of a query processing operation in accordance with an embodiment of the present invention;

FIG. 10(a) is a diagram illustrating an exemplary relationship between the processor and coprocessor with respect to how interactions with a relational database are performed;

FIGS. 10(b) and (c) illustrate an exemplary flowchart and an exemplary logical view of a query processing operation in accordance with an embodiment of the present invention corresponding to FIG. 10(a);

FIGS. 11(a)-(g) illustrate exemplary data flows within the search appliance of FIG. 4 for a query processing operation in accordance with an embodiment of the present invention;

FIG. 12 illustrates an exemplary overview of a query processing operation in accordance with an embodiment of the present invention wherein the query is run at least partially against documents that are located outside the search appliance;

FIG. 13 illustrates an exemplary overview of a query processing operation in accordance with an embodiment of the present invention wherein an RDBMS external to the search appliance is accessed to retrieve structured data specified by a query;

FIG. 14 illustrates an exemplary logical view of a query processing operation in accordance with an embodiment of the present invention wherein an external RDBMS is accessed to retrieve structured data specified by a query;

FIGS. 15(a)-(h) illustrate exemplary data flows within the search appliance of FIG. 4 for a query processing operation in accordance with an embodiment of the present invention wherein an external RDBMS is accessed to retrieve structured data specified by a query;

FIG. 16 illustrates an exemplary processing flow for an API executed by the search appliance to process queries;

FIGS. 17(a) and (b) illustrate examples of how FAM pipelines can be deployed on a reconfigurable logic device to carry out a document ingest pre-processing operation and a query-specified data processing operation; and

FIGS. 18(a) and (b) illustrate an exemplary embodiment wherein the structured data and the unstructured data are stored in a common data store.

FIG. 19(a) illustrates an exemplary metadata generating appliance in accordance with an embodiment of the present invention;

FIG. 19(b) illustrates an exemplary metadata generating appliance in accordance with another embodiment of the present invention;

FIG. 19(c) illustrates an exemplary metadata generating appliance in accordance with yet another embodiment of the present invention;

FIG. 20(a) is a high level block diagram view of how a coprocessor can be used to generate metadata for data that streams therethrough;

FIG. 20(b) depicts an exemplary index that can be created from the metadata produced by the coprocessor of FIG. 20(a);

FIG. 21 depicts an exemplary firmware pipeline that can be deployed within reconfigurable logic to perform a desired metadata generation operation;

FIG. 22(a) depicts an exemplary firmware pipeline that can be used to perform the stream pre-processing operation shown in FIG. 21;

FIG. 22(b) depicts an exemplary output stream of words and feature vectors that can be produced by the firmware pipeline of FIG. 22(a);

FIG. 23(a) depicts an exemplary specialized index generation operation that seeks to exactly match the words within the incoming data stream against the words in a dictionary to thereby generate a specialized index;

FIG. 23(b) depicts an exemplary specialized index generation operation that seeks to approximately match the words within the incoming data stream against the words in a dictionary to thereby generate a specialized index;

FIG. 24(a) depicts an exemplary specialized index generation operation that seeks to exactly match the words within the incoming data stream against the words in a plurality of different dictionaries to thereby generate a plurality of specialized indexes;

FIG. 24(b) depicts an exemplary specialized index generation operation that seeks to match the words within the incoming data stream against a plurality of different regular expression patterns to thereby generate a plurality of specialized indexes;

FIG. 24(c) depicts an exemplary specialized index generation operation that seeks to match the words within the incoming data stream against the words in a plurality of different dictionaries and against a plurality of different regular expression patterns to thereby generate a plurality of specialized indexes;

FIG. 25 depicts an exemplary technique for inserting the generated indexes into operational indexes maintained by a relational database;

FIG. 26(a) depicts a stream pre-processing firmware pipeline that includes a word counting module;

FIG. 26(b) depicts an exemplary index that includes metadata relating to the word counting function of FIG. 26(a);

FIG. 26(c) depicts an exemplary firmware pipeline wherein the generated metadata corresponds to classification data about the incoming documents;

FIG. 27(a) depicts an exemplary classification operation that produces class scores for incoming documents to assess their relevance to a plurality of different classifications;

FIG. 27(b) depicts an exemplary classification operation that determines a classification for each incoming document;

FIGS. 28(a) and (b) depict exemplary firmware pipelines that employ histogram generation to enable document clustering;

FIGS. 29(a) and (b) depict exemplary firmware pipelines that perform classification, specialized index generation, and histogram generation operations on incoming documents; and

FIG. 30 depicts an exemplary environment in which the appliance of FIGS. 19(a)-(c) can be employed.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 2 depicts an overview of a preferred embodiment of the present invention, wherein an enterprise appliance 200 is configured to provide a user of user computer 100 with intelligent and unified access to structured data (such as that stored by relational database 210) and unstructured data (such as that stored and accessible through components 104, 106 and 108 or through the Internet 110). While an embodiment of appliance 200 can be referred to as a search appliance, it should be noted that data analysis functionalities other than or in addition to searching can be supported by appliance 200, as explained herein.

Preferably, search appliance 200 employs a hardware-accelerated data processing capability as well as a query handling API for handling queries directed at least partially toward structured data. FIG. 4 depicts a preferred embodiment for the appliance 200. Within appliance 200, a coprocessor 450 is positioned to receive data that streams off either or both a disk subsystem defined by disk controllers 414 and 416 and by data stores 304 and 306 (either directly or indirectly by way of system memory such as RAM 408) and a network 420 (via network interface 410). Data store 304 comprises an RDBMS in which structured relational data is stored, while data store 306 comprises a file system in which unstructured data is stored. It should be noted, however, that unstructured data may optionally also be stored in an unstructured data column within RDBMS 304, as described below in connection with FIGS. 18(a) and (b). Network 420 preferably comprises an enterprise network (whether LAN or WAN), in which various disparate document stores 308 (e.g., components 104, 106 and/or 108) are located. While data store 304 is characterized as a data store for structured data, it should be noted that data store 304 may optionally contain unstructured data blobs which can also be subjected to ingest and query processing.

In a preferred embodiment, the coprocessor 450 comprises a reconfigurable logic device 402. Preferably, data streams into the reconfigurable logic device 402 by way of system bus 406, although other design architectures are possible (see FIG. 7(b)). Preferably, the reconfigurable logic device 402 is a field programmable gate array (FPGA), although this need not be the case. System bus 406 can also interconnect the reconfigurable logic device 402 with the appliance's processor 412 as well as the appliance's RAM 408. In a preferred embodiment, system bus 406 may be a PCI-X or PCI-Express bus, although this need not be the case.

The data store 306 can be any data storage device/system, but is preferably some form of a mass storage medium. For example, the data store 306 can be a magnetic storage device such as an array of disks. However, it should be noted that other types of storage media are suitable for use in the practice of the invention.

The computer system defined by processor 412 and RAM 408 can be any commodity computer system as would be understood by those having ordinary skill in the art. For example, the computer system may be an Intel Xeon system or an AMD Opteron system. Thus, processor 412, which serves as the central or main processor for appliance 200, preferably comprises a GPP.

The reconfigurable logic device 402 has firmware modules deployed thereon that define its functionality. The firmware socket module 404 handles the data movement requirements (both command data and target data) into and out of the reconfigurable logic device, thereby providing a consistent application interface to the firmware application module (FAM) chain 350 that is also deployed on the reconfigurable logic device. The FAMs 350i of the FAM chain 350 are configured to perform specified data processing operations on any data that streams through the chain 350 from the firmware socket module 404. Preferred examples of FAMs that can be deployed on reconfigurable logic in accordance with a preferred embodiment of the present invention are described below.

The specific data processing operation that is performed by a FAM is controlled/parameterized by the command data that FAM receives from the firmware socket module 404. This command data can be FAM-specific, and upon receipt of the command, the FAM will arrange itself to carry out the data processing operation controlled by the received command. For example, within a FAM that is configured to perform an exact match operation, the FAM's exact match operation can be parameterized to define the key(s) that the exact match operation will be run against. In this way, a FAM that is configured to perform an exact match operation can be readily re-arranged to perform a different exact match operation by simply loading new parameters for one or more different keys in that FAM.

Once a FAM has been arranged to perform the data processing operation specified by a received command, that FAM is ready to carry out its specified data processing operation on the data stream that it receives from the firmware socket module. Thus, a FAM can be arranged through an appropriate command to process a specified stream of data in a specified manner. Once the FAM has completed its data processing operation, another command can be sent to that FAM that will cause the FAM to re-arrange itself to alter the nature of the data processing operation performed thereby. Not only will the FAM operate at hardware speeds (thereby providing a high throughput of target data through the FAM), but the FAMs can also be flexibly reprogrammed to change the parameters of their data processing operations.

The FAM chain 350 preferably comprises a plurality of firmware application modules (FAMs) 350a, 350b, . . . that are arranged in a pipelined sequence. However, it should be noted that within the firmware pipeline, one or more parallel paths of FAMs 350i can be employed. For example, the firmware chain may comprise three FAMs arranged in a first pipelined path (e.g., FAMs 350a, 350b, 350c) and four FAMs arranged in a second pipelined path (e.g., FAMs 350d, 350e, 350f, and 350g), wherein the first and second pipelined paths are parallel with each other. Furthermore, the firmware pipeline can have one or more paths branch off from an existing pipeline path. A practitioner of the present invention can design an appropriate arrangement of FAMs for FAM chain 350 based on the processing needs of a given application.

A communication path 430 connects the firmware socket module 404 with the input of the first one of the pipelined FAMs 350a. The input of the first FAM 350a serves as the entry point into the FAM chain 350. A communication path 432 connects the output of the final one of the pipelined FAMs 350m with the firmware socket module 404. The output of the final FAM 350m serves as the exit point from the FAM chain 350. Both communication path 430 and communication path 432 are preferably multi-bit paths.

The nature of the software and hardware/software interfaces used by appliance 200, particularly in connection with data flow into and out of the firmware socket module are described in greater detail in the above-referenced and incorporated U.S. Patent Application Publication 2007/0174841.

FIG. 7(a) depicts a printed circuit board or card 700 that can be connected to the PCI-X or PCI-e bus 406 of a commodity computer system for use as a coprocessor 450 in appliance 200 for any of the embodiments of FIGS. 2(a)-(c). In the example of FIG. 7(a), the printed circuit board includes an FPGA 402 (such as a Xilinx Virtex II FPGA) that is in communication with a memory device 702 and a PCI-X bus connector 704. A preferred memory device 702 comprises SRAM and DRAM memory. A preferred PCI-X or PCI-e bus connector 704 is a standard card edge connector.

FIG. 7(b) depicts an alternate configuration for a printed circuit board/card 700. In the example of FIG. 7(b), a bus 706 (such as a PCI-X or PCI-e bus), one or more disk controllers 708, and a disk connector 710 are also installed on the printed circuit board 700. Any commodity disk interface technology can be supported, as is understood in the art. In this configuration, the firmware socket 404 also serves as a PCI-X to PCI-X (or PCI-e to PCI-e) bridge to provide the processor 412 with normal access to any disk(s) connected via the private PCI-X or PCI-e bus 706. It should be noted that a network interface can be used in addition to or in place of the disk controller and disk connector shown in FIG. 7(b).

It is worth noting that in either the configuration of FIG. 7(a) or 7(b), the firmware socket 404 can make memory 702 accessible to the bus 406, which thereby makes memory 702 available for use by an OS kernel as the buffers for transfers to the FAMs from a data source with access to bus 406. It is also worth noting that while a single FPGA 402 is shown on the printed circuit boards of FIGS. 7(a) and (b), it should be understood that multiple FPGAs can be supported by either including more than one FPGA on the printed circuit board 700 or by installing more than one printed circuit board 700 in the appliance 200. FIG. 8 depicts an example where numerous FAMs in a single pipeline are deployed across multiple FPGAs.

In the exemplary embodiments discussed herein, the term “documents” will be used to describe the unstructured data that is processed through the inventive system. However, it should be noted that the usage of the term “documents” is meant to be only exemplary as other forms unstructured data can also be processed using the inventive system and method.

An optional configuration that may enhance performance of appliance 200 is the ability to bring a large number (possibly all) of an enterprise's documents into its on-board data store 306. Furthermore, it is preferred that in doing so, the appliance 200 build metadata about each document that it ingests. This document metadata comprises structured data that can then be stored in a relational database system such as in on-board RDBMS 304.

FIG. 3 depicts an overview of document ingest pre-processing in accordance with an embodiment where appliance 200 is used to index documents within an enterprise. Preferably, through some form of document ingest GUI 300 displayed on user computer within enterprise network 420, the user is able to specify which document(s) should be ingested into data store 306. Optionally, the user can also key in various forms of metadata about the document(s) to be ingested; however this need not be the case as the coprocessor 450 can be arranged to perform the desired metadata generation operations automatically. In response to an appropriate user command 312 delivered to appliance 200 from GUI 300, one or more documents 312 stored in a data store 308 accessible through the enterprise network 420, but external to the appliance 200, is delivered to the appliance 200. Various adapters can be employed in document retrieval functionality 352 employed by the appliance 200 to provide access to documents stored on common file systems such as NTFS, FAT, CIFS, various flavors of Unix file systems, as well as Web access over HTTP.

FAMs resident in firmware pipeline 350 are preferably arranged to perform a document metadata generation operation on the documents it receives. Examples of document metadata generation techniques that could be employed in firmware 350 include but are not limited to part-of-speech tagging, information and entity extraction, document classification, document clustering, and text summarization. Functionally, these operations can be viewed as a series of “transformations” on a data stream of one or more documents. One illustrative example of a document classification operation that can be performed on documents includes language classification. With language classification, the document can be applied to a statistical n-gram algorithm that is configured to identify the language that the text within the document most closely matches. Other document classification operations can employ hidden Markov models (HMMs) to learn some type of classification for the document. Further still, regular expression pattern matching can be employed by the firmware 350 to further develop classification information about a document. As an example, a document classifier that can be used may be a flag that identifies whether the document in question contains a credit card number; in such cases, firmware 350 can include FAMs that implement a regular expression pattern matching operation, wherein this regular expression pattern matching operation is keyed around determining whether a document streamed therethrough includes a data pattern that looks like a credit card number. Based on the results of this operation, the credit card indicator metadata can be set to pFositive or negative.

It is believed that conventional approaches to metadata generation operations have built these operations into software executed by a main processor such as processor 412, which as explained above, exhibits performance shortcomings. The inventors believe that by offloading these metadata generation operations to a coprocessor 450, significant acceleration can be achieved. Additional details regarding the use of coprocessors to perform metadata generation operations can be found in the above-referenced and incorporated U.S. patent application Ser. No. 11/938,732 entitled “Method and System for High Performance Data Metatagging and Data Indexing Using Coprocessors” and as described below.

The document metadata 314 that is produced by the operation of firmware 350 can then be stored in RDBMS 304, wherein the RDBMS engine operates to generate and maintain an index of this document metadata that can later be queried using standardized database queries to identify which documents within data store 306 should be processed through the coprocessor 450 at hardware speeds during query processing. After the received document 312 has been processed by firmware 350, document 312 can then be ingested into the appliance by storing it in the data store 306 of unstructured data. The acts of metadata generation and document ingestion preferably operate virtually concurrently in near-real time. It should be noted that the document metadata 314 can optionally be stored in a structured database external to the appliance 200.

FIG. 5 depicts this document ingest pre-processing as a logical flow. At step 1, the user interacts with GUI 300 to identify a new document 312 for ingestion into appliance 200. This can include the user configuring a new document data source. Furthermore, this configuration can include fields to extract, classification hierarchies, or named entities. GUI 300 may optionally be configured to allow the user to specify what metadata is to be generated from document 312. Next, at step 2, the document 312 is retrieved from its original source location (either an enterprise document store 308, the Internet or some other network that is accessible to the enterprise network 420). Then, firmware 350 performs its document metadata generation operation 500 on document 312 to produce document metadata 314. At step 3, the document 312 is then stored in the file system of data store 306 (see the upper branch of the flow labeled “3” in FIG. 5), and the document metadata (including its location in the file system of data store 306) is saved into relational tables of the RDBMS 304 (see the lower branch of the flow labeled “3” in FIG. 5). As shown in FIG. 5, a document, in its native format, can be brought into high-performance storage on a file system within the appliance for the upper portion of step 3. Furthermore, the lower portion of step 3 in FIG. 5 shows how the document metadata from the pre-processing can be saved in the relational tables in a database inside the appliance. This step can leverage FPGA processing, and it adds little or no latency. FIG. 6 illustrates this data flow superimposed over appliance 200.

In this manner, appliance 200 can now use the document metadata 314 indexed by RDBMS 304 to aid decisions as to which documents should be subjected to a query-specified data processing operation (e.g., a full-text searching operation) via coprocessor 450. Furthermore, because standardized RDBMS technology has been leveraged within appliance 200, standardized database queries that are well-known to a number of users can be used to decide which documents are to be subjected to the coprocessor-based data processing operation when processing a given query 904.

It is generally preferred that the relational database 304 use indexing methodologies such as B-tree indexes to optimize queries of the document metadata 314. Also, because of the richness of the indexes that can be generated via the hardware-accelerated metadata generation. Sophisticated full-text searching operations, including proximity searching (i.e., finding instances where Word X is separated from Word Y by less than Z word positions), can be efficiently accomplished by leveraging the power of the indexes.

Furthermore, when an enterprise adopts appliance 200 to aid its enterprise data handling operations, the document ingest pre-processing can be applied not only prospectively to newly-created documents but also retroactively to all or a significant subset of the enterprise's already existing documents. Thus, when installing the appliance 200, an enterprise may want to ingest all or a significant subset of its documents through the appliance as specified in connection with FIGS. 3, 5 and 6 to enable effective and efficient document searching. However, it should be noted that the documents subjected to the ingest pre-processing described in connection with FIGS. 3, 5 and 6 need not be limited to documents external to appliance 200. The pre-processing can also be applied to documents within data store 306 that had not previously been subjected to the metadata generation operation or to documents in need of new metadata generation operations.

It should also be noted that the storage 308 from which documents are ingested into appliance 200 for pre-processing can be any data store accessible through the enterprise network (e.g., enterprise data stores within enterprise network 420 and data stores that are external to the enterprise network but nevertheless accessible thereto). For example, the documents that are ingested into appliance 200 can be Internet content such as web pages.

Once document metadata 314 for a significant number of documents 312 has been stored in RDBMS 304, the appliance 200 is now ready to handle user-specified queries. An API within appliance 200 is preferably configured to enable the appliance to process standardized database queries against the document metadata 314 in RDBMS 304 and then use the result set of the query to determine which documents should be delivered to coprocessor 450 for a query-specified data processing operation.

FIG. 9 depicts an exemplary overview of how such queries can be processed. A conventional BI reporting tool 900 can be accessed by user on his/her desktop, and through this tool 900, the user can enter a desired query 904 using whatever syntax is already familiar to him/her as part of his/her training in the use of the reporting tool 900. Reporting tool 900 then operates to produce a standardized database query (such as a SQL command 906) from the user-specified query 904. Search appliance 200 is positioned to receive this standardized database query 906. The appliance 200 can receive such queries directly from the BI reporting tool 900 (should the BI reporting tool 900 be connected to bus 406) or indirectly from the BI reporting tool 900 through network interface 410. An API 902 executed by the search appliance 200 then operates to appropriately apply the SQL command 906 against the RDBMS 304 and data store 306. Preferably, the operations of the API 902 are carried out by processor 412 of the appliance. However, it should be noted that at least portions of the API functionality can optionally be deployed via the coprocessor 450. Preferably, this API 902 conforms to existing ANSI SQL standards and extensions (e.g., SQL/XML, SQL/MED, SQL/MM or XML/Full-Text) where possible. Where SQL standards and extensions do not support the desired functionality, external functions can be devised for the API (which could be categorized as “external procedures” in database terminology). FIG. 10(a) illustrates a preferred embodiment for API 902. FIG. 16 discussed hereinafter describes an alternate embodiment of API 902.

Thus, a preferred embodiment of the present invention operates to provide SQL-enabled client applications with the ability to access the hardware-accelerated functionality of coprocessor 450 via SQL commands. Thus, not only can the appliance 200 be integrated with a SQL-enabled client application such as a BI reporting tool 900, but the appliance 200 can also or alternatively be integrated with other SQL-enabled applications. For example, the appliance 200 can be integrated with any or all of the following SQL-enabled applications: various enterprise software applications (such as ERP, CRM, SAP, etc.), middleware programs, client programs, custom-built programs written in any of a large number of programming languages (e.g., using ODBC or JDBC connectivity), and another SQL database that is linked to database 304.

Within the appliance 200 itself, the SQL-enablement preferably includes a high-performance, tight integration with conventional SQL relational engine software 950. An example of this is shown in FIG. 10(a). Relational engine software 950 can be conventional off-the-shelf software for accessing relational databases. To integrate query processing through the relational engine 950 with the coprocessor 450, some customizations can be added to relational engine software 950. A few exemplary methods that could yield this type of customization to achieve the desired integration are: C-based external procedures (custom libraries dynamically linked into the SQL engine), user-defined types and functions, stored procedures, and custom data providers.

For example, code can be added to the relational engine 950 that invokes a desired external procedure when certain statements are encountered in a SQL command. An example of this is shown in FIG. 10(c), wherein the relational engine 950 is configured to recognize the statement “text_contains” as invoking an external program (shown as coprocessor interface software 952 in FIG. 10(a)). When the relational engine 950 encounters such a statement, it makes a call on the coprocessor interface software 952 and passes the appropriate data to the API software 952 to thereby allow the coprocessor to function as desired, as explained below in connection with FIG. 10(b). It should be readily understood that a number of external procedures can be devised for relational engine 950 such that different statements encountered in a SQL command will invoke different external programs to thereby achieve different processing effects with the coprocessor 450. As stated, the “text_contains” statement can be tied to an external procedure that configures the coprocessor for an exact or approximate matching operation, while a “relevance ranking” statement can be tied to an external procedure that configures the coprocessor for scoring data objects along relevance dimensions.

If the appliance 200 is implemented with an open-source database 304, such as MySQL, then the integration can be achieved directly within the relational engine source code itself. With the greater flexibility that an open-source solution provides, a SQL parser/interpreter can be developed that serves as the API 902 and intermediates all SQL requests between the client application and the database 304. An exemplary implementation of a SQL parser/interpreter strategy for API 902 is documented in FIG. 16.

Returning to the embodiment of FIG. 10(a), FIG. 10(b) presents a series of steps that could be used to implement a query-processing solution that is based on such standard SQL extensions such as stored procedures, external procedures or user-defined functions. It is closely tied to FIG. 10(c), which uses the same series of steps (1101-1170). At step 1101, an ANSI-standard SQL command 906 is composed and invoked via a SQL-enabled client application. Next, at step 1110, the relational engine 950 executes on processor 412, and it parses the SQL command 906 to decide how the RDBMS 304 should be queried. Optimizer hints and various coding techniques allow the SQL developer to construct a command where the order of processing can be guaranteed. That is, the optimizer hints can define an appropriate order of processing as between various statements in a SQL command 906. With reference to FIG. 10(c), this would involve the relational engine satisfying the “date_loaded” statement prior to the working on the “text_contains” statement. The task at hand is to try to use indexed tables stored by the RDBMS 304 to limit the objects that will need to be full-text scanned by coprocessor 450. Essentially, the relational engine 950 will apply a portion of the query that is directed toward structured data to RDBMS 304 (this query portion is identified as SQL command 908 in the example of FIG. 9 and FIG. 11(b)). The relational engine and RDBMS 304 can thus operate on a SQL command 908 to identify documents listed in a document metadata index that it maintains to thereby identify which document match the query conditions. Thus, at step 1120, the RDBMS 304 will return a list 910 of documents after matching the criteria set forth in the “date_loaded” constraint portion of the SQL command 906 against the content of its document metadata index. The documents identified by document list 910 can preferably be identified by their locations in data store 306. At step 1125, the relational engine 950 next encounters the “text_contains” statement, which is recognized as invoking an external procedure. The relational engine 950 then invokes the coprocessor interface software 952 that is tied to the “text_contains” statement. The relational engine 950 passes the query string following the “text_contains” statement to the coprocessor interface software 952 and further informs the coprocessor interface software 952 of the file list 910 generated at step 1120. The coprocessor interface software 952 in turn directs the operation of the coprocessor, preferably by passing the query string to the coprocessor 450 together with a command that instructs the coprocessor to appropriately configure itself to perform the query-specified data processing operation. Then, at step 1130, the entire bodies of the unstructured documents identified by list 910 are read into the coprocessor 450. Preferably, coprocessor interface software 952 issues an instruction to the disk controller 416 to stream the unstructured documents on list 910 out of data store 306. The data store 306 then provides the requested documents to the coprocessor 450 as a data stream 914 that is processed therethrough. Coprocessor 450 then performs the specified data processing operation on data stream 914 at hardware speeds (step 1140), providing significant acceleration to the query handling operation relative to conventional approaches. Any “hits” that are found by the coprocessor 450 can then be returned by the coprocessor to a temporary database table in RAM 408 as a result set 916 (step 1150). The coprocessor interface software 952, in turn, can inform the relational engine 950 of this result set 916. Optionally, at step 1160, the relational engine 950 can post-process these results 916 to perform any desired aggregations, cross-correlations, or subsequent analysis on the results 916.

Next, at step 1170, the relational engine 950 preferably formats the search results 916 to a format expected by the reporting tool 900, and the reporting tool 900 operates using its existing technology to present those search results 916 to the user.

As there are a wide variety of BI reporting tools 900 used in industry, the API 902 is preferably configured with the capability to interface with at least most of the major BI reporting tools. For example, a configuration file maintained by the search appliance 200 can be set up during initialization of search appliance 200 within an enterprise to identify the particular BI reporting tool 900 with which the search appliance 200 will interact to enable data exchanges therebetween.

It should also be noted that a conventional BI reporting tool 900 need not be used as the interface between the search appliance 200 and the user. For example, search appliance 200 can be configured to provide its own GUIs for display to users, wherein these GUIs are configured to provide the same basic functionality as BI reporting tools. In this manner, the API 902 could then optionally be configured to directly translate the user-specified query 904 into the database query 908.

Further still, it should be noted that the standardized query 906 need not originate from a BI reporting tool 900 or user; instead it can originate from some other enterprise application that is making a call on the data stored by or known to the search appliance 200.

Also, it should be noted that the API 902 described herein as part of the search appliance 200 can optionally be located, in whole or in part, within the BI reporting tool 900 or other higher level application.

FIG. 10(c) illustrates a logic view of a simple query processing operation in accordance with a preferred embodiment of the invention. In this example, the user wants to search for documents within data store 306 that were loaded on Jul. 7, 2007 and that contain the following text constraint: the phrase “high throughput” near the word “blastn”. After the user enters a query toward this goal in a BI reporting tool 900, the BI reporting tool operates to generate a SQL command 906 such as the one shown in FIG. 10(c). This SQL command includes a “select” statement that specifies the table within RDBMS 304 against which the query is to be processed. The next statement is a “where” statement that specifies the conditions of the search. One of the conditions is the date on which the document was loaded into the data store 306; this condition is set to Jul. 7, 2007. The next condition is the text condition discussed above. The relational engine receives this SQL command 906 and interprets it, as shown in FIG. 10(c) (see step 1101; see also FIG. 11(a)).

The relational engine 950 identifies the “date_loaded” constraint as a document metadata item and further identifies the text constraint as a matter to be resolved by the coprocessor 450, as discussed above. With respect to the embodiment of FIGS. 10(a) and (b), the relational engine 950 queries the RDBMS 304 using SQL command 908 which corresponds to the “date_loaded” portion of SQL command 906 (see step 1110; see also FIG. 11(b)). A documents table in the relational database inside the appliance serving as the metadata index 314 will have had metadata generated for it when documents were loaded into storage. Filenames for responsive documents can be retrieved for all rows matching the criteria in the SQL “where” clause.

The RDBMS then returns a list 910 of all documents identified by the metadata index 314 as matching the “date_loaded” constraint (i.e., RDBMS 304 would then apply this SQL command 908 against its document metadata index to return a list of all documents that were loaded into data store 306 on Jul. 7, 2007), wherein this list 910 can be stored in RAM 408 (for example, a temporary database table built in memory to store the list of filenames which matched the “where” clause criteria). (See step 1120; see also FIG. 11(c)). This list 910 preferably identifies the locations in the file system of data store 304 where each document loaded on Jul. 7, 2007 is located.

The API 902 (via API 952 for the embodiment of FIGS. 10(a) and (b)—see step 1125) also issues a request 912 to data store 306 for retrieval of all documents on list 910 (see FIG. 11(d)). The API 902 also operates (via API 952 for the embodiment of FIGS. 10(a) and (b)—see step 1125) to generate a control signal 1100 for delivery to the coprocessor's FAM pipeline 350 that arranges the FAM pipeline to perform a full-text search built around the conditions of “‘high throughput’ near ‘blastn’” from the query string. This control signal 1100 is then preferably delivered to the coprocessor 450 (preferably delivered to a firmware socket module 404 resident on the coprocessor 450) prior to the documents' arrival to the coprocessor 450 (see FIG. 11(e)).

In response to the request 912, data store 306 outputs a data stream 914 for delivery to the coprocessor 450 (preferably to the firmware on reconfigurable logic device 402), as shown in FIG. 11(e) (see also step 1130). The coprocessor 450 (preferably via a FAM pipeline 350 on reconfigurable logic device 402) then performs a hardware-accelerated full-text search of the documents within stream 914 in accordance with the text constraints in the query (see step 1140; see FIG. 11(f)). The search results 916 can take the form of another temporary database table built in memory to store the matches returned from the coprocessor during the full-text search. The results of this high speed data processing operation are then returned to the API 902 by way of the firmware socket module 404 (see step 1150). The API 902 (preferably relational engine 950) then operates to format those search results 916 in a manner expected by the reporting tool 900 for return to the reporting tool 900 where they can be presented to the user in satisfaction of his/her query, as shown in FIG. 11(g) (see step 1170).

It should also be noted that the documents 312 for which document metadata 314 has been generated need not necessarily be stored within the appliance in data store 306. Those documents can be retained in their original locations outside the appliance 200 if desired. In such instances, when it is time for those documents to be full-text processed via coprocessor 450, those documents can streamed into appliance 200 and coprocessor 450 via network interface 410. FIG. 12 is a counterpart to FIG. 9 that depicts this aspect of document searching, wherein the documents on list 910 returned by RDBMS 306 include both documents inside data store 306 and documents external to appliance 200 that are located in some other data store 308 that is accessible through the enterprise network. In such cases, two requests 912a and 912b will be prepared by the API 902—one for delivery to data store 306 and the other for delivery outside the appliance 200. While this arrangement is less desired because network bandwidths may then constrain the latency with which searches are performed, the inventors nevertheless note that some acceleration is still provided even if the documents are not retained within the data store 306. In this vein, it is also worth noting that the act of ingesting a document 312 into data store 306 can be a copy operation rather than a move operation. Some enterprises may prefer that the master copy of the document 312 remain in its original location external to appliance 200. In such situations, only a copy of the document 312 is stored by the data store 306.

Another powerful aspect of the preferred embodiment is the ability of the appliance 200 to access any enterprise RDBMSs 1300 that are external to the search appliance 200 when performing data processing operations. An overview of this aspect of the preferred embodiment is shown in FIG. 13. As part of this aspect of the preferred embodiment, SQL commands 1302 will be issued by the API 902 to the external RDBMS 1300, and a response 1304 to those commands will be received by the API 902. Accordingly, the appliance 200 will be able to efficiently leverage existing structured data maintained by an enterprise when performing searches for documents of interest.

FIG. 14 depicts a logical view of this aspect of SQL command processing. In the example of FIG. 14, the user has been tasked with the assignment to research why sales have been slow as of late to certain customers of the enterprise. As part of this task, the user would like to review documents stored by the network that may provide useful insights into such sluggish sales. To achieve this, the user specifies a query targeted toward finding all documents for any customers whose monthly sales amount is less than 10,000 widgets, wherein those documents were loaded on Jul. 7, 2007, and wherein those documents contain the text “trouble” near either “widget” or “new product”. The BI reporting tool 900 operates to transform these query constraints into a SQL command 906 as shown in FIG. 14.

Because the enterprise stores its customer sales data in an RDBMS 1300 that is external to the search appliance 200, SQL command 906 operates to join data tables in external RDBMS 1300 with document metadata tables in RDBMS 304. This action is reflected in the “inner join” statement that joins the customer data in the “Customers@external_DB C” relational table in an external RDBMS 1300 with the document metadata relational table in RDBMS 304, based on a merged key of “D.Customer_ID” (for the document metadata table) and “C.Customer_ID” (for the external relational table), a SQL operation that is well-known in the art. Based on this merge, the relational engine 950 can identify from the external relational table which customers have sales volumes below 10,000 and tie those customers to a field in the document metadata table. Then the documents for those customers can be further restricted based on the “date loaded” metadata field in the document metadata. Finally, the documents for customers meeting the sales volume and date loaded constraints can be processed within coprocessor 450 for high speed text mining based on the “‘trouble’ near ‘widget’ or ‘new product’” constraint. Thereafter, processing can proceed as described in connection with FIG. 10(b).

FIG. 15(a) depicts the receipt by the API 902 of the SQL command 906, which mirrors that of FIG. 11(a). The relational engine 950 identifies which constraint(s) in the SQL command 906 are targeted toward the external RDBMS 1300 and generates a new SQL command 1302 directed toward the external relational data constraint portions of SQL command 906 (in the exemplary SQL command 906 of FIG. 14, this external constraint portion is the sales volume constraint). The relational engine 950 applies the new SQL command 1302 against the external RDBMS 1300 for processing thereby (see FIG. 15(b)). Thereafter, relational engine 950 receives the result set 1304 from the external RDBMS's processing of SQL command 1302 (see FIG. 15(c)).

The relational engine 950 then continues its handling of SQL command 906 and determines whether any additional constraints are remaining from command 906 that are directed toward RDBMS 304. If there are not, then a SQL command 908 is built for RDBMS 304 based on the customers in result set 1304. If there are, then a SQL command 908 is built for RDBMS 304 based on both the result set 1304 and any remaining internal RDBMS-directed constraints (e.g., the “date loaded” constraint” in the example of FIG. 14). Thus, for the exemplary SQL command 906 of FIG. 14, the relational engine would apply a SQL command that seeks out all documents with document metadata restricted by the customers in result set 1304 for their customer field and restricted by the Jul. 7, 2007 date for their date loaded field. This new SQL command can be delivered to RDBMS 304 for processing thereby (see FIG. 15(c)). In the example of FIG. 14, the filenames retrieved from the metadata index 314 can be the filenames for all rows matching the criteria in the SQL “where” clause (including the companies for which sales are below 10,000 as determined from external RDBMS 1300).

Upon receiving the document list 910 in response to command 908, the remainder of the query processing proceeds as explained above in connection with FIGS. 11(c)-(g), as shown in FIGS. 15(d)-(h). In this example, the control signal 1100 for the FAM pipeline 350 will be configured to arrange the FAM pipeline 350 to perform a full-text search of the documents within data stream 914 to identify which ones contain the text “trouble” near either “widget” or “new product”.

As previously noted, FIG. 16 discloses an alternate embodiment for API 902. With respect to the working example of FIGS. 11(a)-(g), steps 1600, 1602, 1604, 1606, 1616, and 1620 correspond to what is shown in FIG. 11(a). Steps 1624 and 1628 correspond to what is shown in FIG. 11(b). Step 1632 corresponds to what is shown in FIG. 11(c). Step 1640 corresponds to what is shown in FIG. 11(d). Steps 1610 and 1636 correspond to what is shown in FIG. 11(e). Step 1648 corresponds to what is shown in FIG. 11(f), and step 1650 corresponds to what is shown in FIG. 11(g).

API 902 also discloses a series of processing steps for use when at least a portion of the structured data is stored in an RDBMS that is external to the appliance 200. With respect to the working example of FIGS. 15(a)-(h), steps 1600, 1602, 1604, 1606, 1616, 1620, and 1626 correspond to what is shown in FIG. 15(a). It should be noted that because the query in this instance is directed partially toward relational data stored in the external RDBMS 1300, the process flow will branch to step 1626 from step 1620. Thereafter, step 1630 corresponds to what is shown in FIG. 15(b). Steps 1634, 1638, 1642, 1644, and 1646 correspond to what is shown in FIG. 15(c). At that point, the FIG. 16 process flow branches to step 1632, and the remaining operations proceed such that FIGS. 15(d)-(h) operate as described in connection with FIGS. 11(c)-(g).

It is also worth noting, that the appliance 200 can also be configured to handle queries from a higher level application such as a BI reporting tool 900 that are directed toward neither documents within data store 304, documents about which RDBMS 304 maintains metadata, or data within RDBMS 304. In such instances, the API 902 essentially acts as a pass through (at least as a pass through to relational engine 950) as those queries are directed toward the appropriate external components (see steps 1604, 1608, 1614, and 1618).

It can also be noted that API 902 can be configured to handle queries that are directed solely toward metadata within RDBMS 304 (e.g., queries on the metadata but that do not include a document text searching constraint), as shown by steps 1606, 1612, 1614 and 1618 in FIG. 16.

FIGS. 17(a) and (b) depict examples of how the FAM pipeline 350 of the reconfigurable logic device 402 can be set up to carry out the hardware-accelerated data processing tasks of the preferred embodiment. In the example of FIG. 17(a), a single FAM pipeline 350 is employed, wherein a first set of FAMs 1700 within the pipeline are configured to carry out the document metadata generation operation and a second set of FAMs 1702 within the pipeline are configured to carry out the query-specified data processing operation (or vice versa). With this arrangement, when the FAM pipeline 350 is being used for document ingest pre-processing, the FAMs directed toward the query-specified data processing can be set to a “pass through” mode such that they are effectively turned off. When the FAM pipeline 350 is otherwise used for a query-specified data processing operation, the FAMs directed toward the document metadata generation operation can then be set to a “pass through” mode such that they are effectively turned off.

As an alternative to this mode of operation, the FAM set 1700 and the FAM set 1702 can both be set up as their own separate individual pipelines, as shown in FIG. 17(b). In this example, intelligence built into the firmware socket module 404 can direct data (control data and target data) to the appropriate FAM set based on what type of processing is needed.

Any of a variety of algorithms can be used for the query-specified data processing operation performed by coprocessor 450 (preferably via firmware 350 deployed on a reconfigurable logic device 402). As mentioned above, full-text searching can be carried out by the coprocessor. Examples of various full-text searching operations that can be performed by the coprocessor include exact match operations, approximate match operations, regular expression matching operations, pattern matching operations, and others. With full-text searching, one or more keys corresponding to the data sought to be found in the unstructured data (as defined by the query) can be loaded into the coprocessor 450 and the streaming unstructured data can be compared against the one or more keys using various techniques to determine whether any of the streaming unstructured data satisfies the query. Exemplary embodiments for such full-text searching operations are disclosed in the above-referenced and incorporated U.S. Pat. Nos. 6,711,558 and 7,139,743 and U.S. Patent Application Publications 2006/0294059, 2007/0130140, and U.S. Patent Application Publication 2007/0260602 entitled “Method and Apparatus for Approximate Pattern Matching” (published from U.S. application Ser. No. 11/381,214, filed May 2, 2006).

Another example of a data processing operation that can be performed by the coprocessor 450 includes biosequence similarity searching, embodiments of which are disclosed in U.S. Patent Application Publication 2007/0067108 and U.S. Patent Application Publication 2008/0086274 entitled “Method and Apparatus for Protein Sequence Alignment Using FPGA Devices” (published from U.S. application Ser. No. 11/836,947, filed Aug. 10, 2007), both of which are referenced and incorporated above.

Further still, a pipeline within the coprocessor 450 can be arranged to perform a plurality of different data processing operations on unstructured data. For example, should unstructured data be stored in data store 306 in an encrypted format, then the coprocessor can be configured with a pipeline that performs a decryption operation on the encrypted unstructured data prior to performing a full-text searching operation. Similarly, should the unstructured data be stored in data store 306 in a compressed format, then the coprocessor can be configured with a pipeline that performs a decompression operation on the compressed unstructured data prior to performing a full-text searching operation. Further still, should the unstructured data be stored in data store 306 in an encrypted and compressed format, then the coprocessor can be configured with a pipeline that performs decryption and decompression prior to performing a full-text searching operation.

It should also be noted that a practitioner of the present invention may choose to employ security features within the appliance 200 that will limit the content that is available to various users. Preferably such security features are integrated with various enterprise security architectures such as LDAP, Active Directory, Single Sign-On. It should also be noted that security functionality can be hardware accelerated through the coprocessor 450 if desired. For example, the granularity of security control can be efficiently implemented at the data level rather than at the document level through the use of coprocessor 450. For example, in a preferred embodiment wherein the coprocessor comprises a reconfigurable logic device 402, firmware 350 can be arranged on the reconfigurable logic device to employ entitlement filtering that effectively masks the passage of restricted data to downstream FAMs in a firmware pipeline that are arranged for a specified data processing operation. For example, regular expression pattern matching FAMs can be employed to mask out certain pieces of data (e.g., names, telephone numbers, credit card numbers) from data as it streams through the firmware 350. Similarly, in an application of the invention to the medical records field, select data within a medical record that is to only be seen by doctors/nurses can be filtered using appropriately-configured firmware to prevent a user who is searching through the medical records but who is not authorized to review certain parts of medical records from gaining access to the restricted data. In this way, the data processing employed by the firmware 350 can not only employ query-specified data processing, but also additional ancillary data processing operations, such as entitlement filtering or other security controls, encryption/decryption (see for example, the encryption/decryption techniques described in the above-referenced and incorporated U.S. Patent Application Publication 2007/0237327), or other data processing operations that support the query-specified data processing operation.

It should also be noted that the query-handling technique of using structured data to identify a subset of unstructured data that is to be analyzed using a coprocessor can also be applied in situations where the structured data and the unstructured data are located in the same data store. Exemplary embodiments for this are shown in FIGS. 18(a) and (b). It may be the case that a relational database table includes columns of unstructured data. An example of this might arise in a relational database which stores call center records. Structured fields of call center data may identify the date a call was received, the caller's name and telephone number, and the name of the call center agent who took the call. These records may also include an unstructured data field that contains free form text of the call center agent's notes about the caller's call. Using the techniques described herein, a query can directed to appliance 200 (see FIG. 18(b)) that seeks to find all call records between Jan. 1, 2008 and Jan. 31, 2008 wherein the call notes contain the word “refund”. The structured data columns can be accessed by API 902 to identify the subset of call records where the call date was during January 2008. Thereafter, all of the call records in the identified subset (or at least all of the unstructured columns in the call records for the identified subset) can be streamed through coprocessor 450 to identify those January 2008 call records which contain the word “refund”.

As mentioned above, the technology described herein can be used to perform metadata generation to accelerate index generation. Additional embodiments for accelerated index generation will now be described.

FIG. 19(a) depicts an exemplary embodiment for a metadata generating appliance 200. While an embodiment of appliance 200 can be referred to as a metadata generating appliance, it should be noted that functionalities in addition to metadata generation can be supported by appliance 200, as explained above and in the above-referenced and incorporated U.S. Pat. No. 7,660,793.

Preferably, appliance 200 employs a hardware-accelerated data processing capability through coprocessor 450 to generate the desired metadata. Within appliance 200, a coprocessor 450 is positioned to receive data that streams into the appliance 200 from a network 420 (via network interface 410). Network 420 preferably comprises an enterprise network (whether LAN or WAN), in which various disparate data sources are located. Such incoming data may comprise both structured and unstructured data as appliance 200 can provide beneficial metadata generation operations for both data types.

As discussed above with reference to FIG. 2, the computer system defined by processor 412 and RAM 408 can be any commodity computer system as would be understood by those having ordinary skill in the art. For example, the computer system may be an Intel Xeon system or an AMD Opteron system. Thus, processor 412, which serves as the central or main processor for appliance 200, preferably comprises a GPP.

In a preferred embodiment, the coprocessor 450 comprises a reconfigurable logic device 402. Preferably, data streams into the reconfigurable logic device 402 by way of system bus 406, although other design architectures are possible (see FIG. 7(b)). Preferably, the reconfigurable logic device 402 is a field programmable gate array (FPGA), although this need not be the case. System bus 406 can also interconnect the reconfigurable logic device 402 with the appliance's processor 412 as well as the appliance's RAM 408. In a preferred embodiment, system bus 406 may be a PCI-X bus or a PCI-Express bus, although this need not be the case.

As discussed above with reference to FIG. 2, the reconfigurable logic device 402 has firmware modules deployed thereon that define its functionality. The firmware socket module 404 handles the data movement requirements (both command data and target data) into and out of the reconfigurable logic device, thereby providing a consistent application interface to the firmware application module (FAM) chain 350 that is also deployed on the reconfigurable logic device. The FAMs 350i of the FAM chain 350 are configured to perform specified data processing operations on any data that streams through the chain 350 from the firmware socket module 404. Preferred examples of FAMs that can be deployed on reconfigurable logic in accordance with a preferred embodiment of the present invention are described below.

Once a FAM has been arranged to perform the data processing operation specified by a received command, that FAM is ready to carry out its specified data processing operation on the data stream that it receives from the firmware socket module. Thus, a FAM can be arranged through an appropriate command to process a specified stream of data in a specified manner. Once the FAM has completed its data processing operation, another command can be sent to that FAM that will cause the FAM to re-arrange itself to alter the nature of the data processing operation performed thereby. Not only will the FAM operate at hardware speeds (thereby providing a high throughput of data through the FAM), but the FAMs can also be flexibly reprogrammed to change the parameters of their data processing operations.

FIG. 19(b) depicts another exemplary embodiment for appliance 200. In the example of FIG. 19(b), appliance 200 includes a relational database management system 304 that is in communication with bus 406 via disk controller 414. Thus, the data that is streamed through the coprocessor 450 may also emanate from RDBMS 304. Furthermore, the metadata generated by coprocessor 450 can be stored as structured data within RDBMS 304 from which it can be used as an index for various data searching/analysis operations. Such indexes can take the form of B-tree indexes. An example of such usage for the appliance 200 of FIG. 2(b) is described above and in the above-referenced and incorporated U.S. Pat. No. 7,660,793.

FIG. 19(c) depicts another exemplary embodiment for appliance 200. In the example of FIG. 19(c), appliance 200 also includes a data store 306 of unstructured data that is in communication with bus 406 via disk controller 416. Thus, the data that is streamed through the coprocessor 450 may also emanate from data store 306. Furthermore, the unstructured data that is streamed through coprocessor 450 to generate metadata therefor can optionally be stored within data store 306. As described above and in the above-referenced and incorporated U.S. Pat. No. 7,660,793, the metadata tables within RDBMS 304 can be used to render data search/analysis operations on the unstructured data stored within data store 306 more efficient.

FIG. 7(a), discussed above, depicts a printed circuit board or card 700 that can be connected to the PCI-X or PCI-e bus 406 of a commodity computer system for use as a coprocessor 450 in appliance 200 of any of FIGS. 19(a)-(c). FIG. 7(b), also discussed above, depicts an alternate configuration for a printed circuit board/card 700 that can be used as a coprocessor 450 in appliance 200 of any of FIGS. 19(a)-(c). It is worth noting that in either the configuration of FIG. 7(a) or 7(b), the firmware socket 404 can make memory 702 accessible to the bus 406, which thereby makes memory 702 available for use by an OS kernel as the buffers for transfers to the FAMs from a data source with access to bus. It is also worth noting that while a single FPGA 402 is shown on the printed circuit boards of FIGS. 7(a) and (b), it should be understood that multiple FPGAs can be supported by either including more than one FPGA on the printed circuit board 700 or by installing more than one printed circuit board 700 in the appliance 200 (see, for example, FIG. 8).

Should the coprocessor employ a reconfigurable logic device 402 with firmware 350 deployed thereon, FAMs resident in firmware pipeline 350 will be arranged to perform a document metadata generation operation on the documents it receives. Examples of these metadata generation operations are described above and below.

FIG. 20(a) depicts at a high level a coprocessor 450 that receives an incoming data stream, generates metadata for the data stream, and outputs the data stream together with the generated metadata. Examples of different hardware-accelerated metadata generation operations will be described in greater detail hereinafter. Any of a number of text mining and text analytic operations can be employed to generate metadata. Some examples of metadata generation operations include but are not limited to part-of-speech tagging, information and entity extraction, document classification, document clustering, and text summarization. In these examples, the data stream under consideration will be a stream of documents. As noted, this usage in only exemplary as data streams other than documents can be readily processed using coprocessor 450 and the techniques described herein. These data streams may comprise unstructured and/or structured data.

FIG. 20(b) depicts an exemplary index 100 that can built from the metadata generated by coprocessor 450. As described in connection with FIG. 1(b), each table entry 102 comprises a term 104 and its associated pointer 106. In the example of FIG. 20(b), each pointer 106 comprises a document identifier D_iand one or more position identifiers p_i. Each document identifier identifies a document in which the term 104 corresponding to that pointer 106 is present. Each position identifier in the pointer identifies a position within the identified document where that term 104 is located. Preferably, the position identifier is represented by a byte offset relative to the start of a document. Thus, the pointers for the term “Azkaban” in FIG. 20(b) establish that “Azkaban” can be found in Document D₁₂at position p₁and in Document D₉₉₁₉at positions p₁and p₂. Any of a number of well-known techniques can be used to map pointers such as the ones shown in FIG. 20(b) to the documents stored within a file system of an enterprise.

FIG. 21 depicts exemplary firmware 350 that can be employed in reconfigurable logic 402 to perform a metadata generation operation 2104 on an incoming stream of documents 2100. An example of a metadata generation operation 2104 includes a stream pre-processing operation 2102. The stream pre-processing preferably comprises various position and feature vector extraction operations. These operations may employ techniques known in the art as cryptographic hashing to provide the term descriptor and tables to enable the values and lists described herein to be generated and exploited.

FIG. 22(a) depicts an exemplary sequence of processing modules for the stream pre-processing stage 2102. A word parsing module 2250 preferably operates to identify white-space and/or punctuation delimited terms in the documents and record their positions. These recorded positions can then be used to help determine word positions within documents (e.g., the p_ivalues in the pointers of FIG. 20(b)). A stop list filter module 2252 preferably operates to remove words from the document stream that appear on a stop list. This may be desirable to remove common words (e.g. “the”, “a”, “an”) for which indexing may not be particularly helpful from the document stream. The stop list is preferably adjustable to control which words will be removed from the document stream. A stemming module 2254 preferably operates to stem words in the document stream to their roots. Examples of stemming operations include removing plurals, removing prefixes, removing suffixes, removing gerunds, etc. It should also be noted that other processing modules can be included in stream pre-processing module 2102 if desired. For example, a spell-checking module can be used to correct possible spelling errors that exist within a word stream.

FIG. 22(b) depicts an exemplary output data stream from the stream pre-processing operation 2102, wherein the stream includes the parsed words 2260 such that each parsed word is accompanied by a position identifier 2258 for that word. Furthermore, a document identifier 2256 is present that identifies when a new document is present within the stream. While the example of FIG. 22(b) shows a serial stream of such data, it should be noted that the document identifiers 2256 and position identifiers 2258 can flow in bit streams within firmware 350 that are parallel to a bit stream of words 2260.

Returning to FIG. 22(a), as words stream through the pre-processing module 2102, a memory containing an index for the documents can be generated. This index 100 can serve as a general index that is populated with entries for each different word encountered by the stream pre-processing module 2102 together with its associated position vector information (e.g., document identifier and position identifier as shown in FIG. 20(b)). If a word that has already been added to general index 100 is encountered again within the stream, then the pointer information in index 100 for that word can be updated to reflect the latest position at which the word has been found.

It should be noted that the memory used to store the index 100 as words stream through the pre-processing module 2102 can be located in any of a number of places. For example, this memory can be located in the available on-chip memory of the FPGA 402. This memory could also be memory device 702 shown in FIGS. 7(a) and (b). Further still, this memory could be RAM 408. This memory could even be a location such as RDBMS 304. Preferably, coprocessor 450 performs the action of updating index 100. Techniques such as hashing can be used to insert new data into the index 100.

It should be noted that each of the modules shown in FIG. 22(a) as well as the stream pre-processing operation 2102 itself is an optional feature. If desired, the metadata generation operation 2104 can be performed on documents 2100 that have not been pre-processed.

FIG. 23(a) depicts an exemplary embodiment for the metadata generation operation 2104 wherein a specialized index generation operation 2350 is also performed. In FIG. 23(a), words and position vectors stream into an exact matching module 2300. This exact matching module 2300 is loaded with the words 2304 from a dictionary 2302. The exact matching modules will use words 2304 as keys against which the streaming words are compared to determine whether any exact matches exist therebetween. As matches are found by module 2300, a specialized index 2360 maintained in the memory is updated with the matching word information and the corresponding pointers for those matching words (see FIG. 20(b)). The position vectors found by the stream pre-processing operation 2102 (e.g., document identifiers 2256 and position identifiers 2258) can be used as the pointer information. Thus, in addition to the general index 100 described in connection with FIG. 22(a), a specialized index 2360 (that is specific as to dictionary 2302) can also be efficiently generated via the data processing operations of coprocessor 450.

Any of a number of exact matching techniques can be used to perform the exact matching operation. For example, the hardware-accelerated matching techniques disclosed in the above-referenced and incorporated U.S. Pat. Nos. 6,711,558 and 7,139,743 and U.S. Patent Application Publications 2006/0294059, 2007/0130140, and 2007/0260602.

Any collection of words can be used as dictionary 2302. Examples of dictionaries 2302 that can be used include large word collections such as full English language dictionaries, full foreign language dictionaries, scientific/technical dictionaries, etc. Smaller word collections can also be used.

FIG. 23(b) depicts an alternate embodiment for index generation wherein an approximate matching module 2310 is used rather than an exact matching module. It may be desirable to employ an approximate matching module 2310 for the index generation operation 2350 to reduce the likelihood that spelling errors and the like do not result in document text being mis-indexed. Examples of hardware-accelerated approximate matching techniques suitable for use as approximate matching module 2310 are also disclosed in the above-referenced and incorporated U.S. Pat. Nos. 6,711,558 and 7,139,743 and U.S. Patent Application Publications 2006/0294059, 2007/0130140, and 2007/0260602. It should be noted that if a spell-checking module as described above is used in the stream pre-processing stage, it may be unnecessary to employ an approximate matching module to accommodate mis-spellings and the like.

It should also be noted that a plurality of exact matching/approximate matching modules 2300/2310 can be deployed in parallel to distribute the workload of matching streaming words to the word keys 804 across multiple matching modules 2300/2310. However, if desired, a single serial pipelined sequence of compare registers can be used by an matching module 2300/2310 to sequentially compare the streaming words with word keys 2304 if desired.

It should be noted that, like the memory used to store general index 100, the memory used to store the specialized indexes 2360 can be located in any of a number of places (e.g., the on-chip memory of the FPGA 402, memory device 702, etc.).

A powerful aspect of various embodiments of the present invention is the ability to concurrently generate multiple specialized indexes. An example of this is shown in FIG. 24(a). With this embodiment, the matching module 2300/2310 matches the incoming words against multiple dictionaries 2302_i, each dictionary 2302_ihaving its own collection of words which will serve as the keys against which the incoming words are judged. As shown, the words within dictionaries such as an English dictionary, a French dictionary, a medical dictionary, and a technical dictionary can be compared against the incoming word stream. As matches are found between the incoming words and the words within any of these dictionaries, a specialized index associated with each of these dictionaries can be updated with pointer information. FIG. 24(a) depicts an English dictionary index 2402 and a technical dictionary index 2404 as examples of such specialized indexes. Preferably, these specialized indexes are pre-populated in the term column with all of the words within its associated dictionary. As that term is found within the word stream by the matching module 2300/2310, the pointer information for that term can be updated as appropriate.

The general index 100 in such an embodiment can be updated when words streaming through the exact matching module do not find a match in any of the dictionaries 2302. Furthermore, the entries in the other specialized indexes can be merged into the general index if desired to thereby make general index 100 applicable to all of the words within document stream 2100. This merger can be performed offline or concurrently with the updating of the other indexes. Otherwise, the coprocessor 450 can be configured to update the general index 100 as words stream out of the stream pre-processing module 2102 and update the specialized indexes as words stream out of the exact/approximate matching module 2300/2310.

Another powerful aspect of various embodiments of the present invention is the ability to perform regular expression pattern matching on incoming words. An example of this is shown in FIG. 24(b). Regular expressions, as is well-known in the art, can be used to detect patterns of varying degrees of complexity within a word stream. An exemplary pattern for which regular expressions are useful means of detection is a social security number (SSN). SSNs exhibit the pattern xxx-xx-xxxx, wherein x can be any number between 0 and 9. Items such as phone numbers, credit card numbers, primary account numbers (PANs), e-mails, URLs, and others also exhibit patterns that are detectable through regular expressions.

As shown in FIG. 24(b), a regular expression matching module 2410 can be deployed on coprocessor 450 (preferably in firmware 350 on a reconfigurable logic device 402). The regular expression matching module 2410 can be configured to detect one or more user-defined patterns. For example, the regular expression matching module 2410 in FIG. 24(b) is configured to detect words (including word portions) that exhibit an e-mail pattern 2412, a phone number pattern 2414, a credit card number pattern 2416, and an SSN pattern 2418. As these patterns are found within the word stream by the regular expression matching module 2410, a specialized index associated with each of these patterns can be updated with the content of the matching pattern and with the pointer information for that matching pattern. FIG. 24(b) depicts an e-mail index 2420 and a SSN index 2422 as examples of such indexes. Thus, if 123-45-6789 is encountered in the word stream, then the regular expression matching module 2410 will identify this pattern as a SSN pattern, and the regular expression matching module 2410 will add an entry to the SSN index 2422 comprising the term “123-45-6789” and a pointer to that term in the word stream. Preferably, as described in connection with FIG. 24(a), a general index 100 is also updated as words stream through the regular expression matching module 2410, and the regular expression matching module 2410 does not find any pattern matches.

An example of a hardware-accelerated regular expression matching technique suitable for use as regular expression matching module 2410 is disclosed in the above-referenced and incorporated U.S. Patent Application Publication 2007/0130140.

Also, as noted above in connection with the matching modules 2300/2310, various combinations of sequential and parallel regular expression matching modules 2410 can be employed within coprocessor 450 to distribute the pattern matching workload across multiple resources.

The power of such pattern indexes shown by FIG. 24(b) cannot be understated. It is typically extremely difficult for a person within an enterprise to efficiently get an answer to a question such as “Which documents within our enterprise include a SSN?” or a command such as “Show me all documents within our enterprise that include a credit card number”. Similarly, it is difficult for a web search engine to provide relevant results to an Internet search query seeking to find web pages which contain credit card numbers or SSNs without being specific as to individual numbers. However, through the indexing techniques disclosed in connection with FIG. 24(b), the answers to questions such as these are readily available.

FIG. 24(c) illustrates an exemplary embodiment wherein the hardware-accelerated index generation operation 2350 includes one or more exact/approximate matching modules 2300/2310 and one or more regular expression matching modules 2410 to generate corresponding specialized indexes (e.g., indexes 2402, 2404, 2420, and 2422) and a general index 100. Once again the power of such efficiently-generated indexes cannot be understated. These indexes can serve to answer complicated query commands such as “Show me all documents which include a credit card number and contain the word ‘stolen’” without re-analyzing the entire set of documents 2100.

It should be noted that coprocessor 450 is preferably configured to change the dictionaries and/or patterns used by the matching modules 2300/2310/2410. Preferably, such changes are made in response to user commands received as command data by the firmware socket module 404. These changes may comprise adding/removing dictionaries/patterns and/or adding/removing words to/from the various dictionaries.

It should also be noted that the coprocessor 450 can deploy these modules 2300/2310 and 2410 in a sequential, parallel, or hybrid sequential-parallel arrangement as desired.

FIG. 25 depicts an embodiment wherein periodic batch transfers of the generated indexes (e.g., indexes 100, 2402, 2404, 2420, and 2422) are performed to merge them into their corresponding operational counterparts within an RDBMS (e.g., RDBMS 304 or some other RDBMS within enterprise network 420). Thus, batch insert/merge operations can be performed to insert the content of general index 100 into operational general index 2500. Similarly, the content of indexes 2402, 2404, 2420, and 2422 can be inserted into operational indexes 2502, 2504, 2520, and 2522 respectively. These transfers can be performed periodically at scheduled intervals or can be performed on an as needed basis as the available memory space for indexes 100, 2402, 2404, 2420, and 2422 runs low.

After the operational indexes have been updated, the newly indexed data can then be accessed via standardized queries such as SQL commands that are applied to the operational indexes stored by SQL databases. The description above and the above-referenced and incorporated U.S. Pat. No. 7,660,793 discloses a particularly efficient query handling technique for queries directed toward unstructured data for which an RDBMS maintains a metadata index.

Another metadata generation operation that can be performed by the stream pre-processing module 2102 is a word counting operation 2650, as shown in FIG. 26(a). In order to ascertain the relevance of the documents 2100 during a query handling operation, the indexing engine preferably obtains statistical information about the words in the documents. This statistical information may include a count of the number of times a particular word appears in a document and a count of the number of times a particular word appears in the entire document collection (should the documents 2100 be grouped into one or more collections). Another parameter that can be determined and used for relevance assessments is the size (in words or bytes) of each document. By knowing these parameters, the relevance of a document retrieved during querying can be calculated using well-known probabilistic relevance models such as the Okapi BM25 model. See Karen Sparck Jones, Steve Walker, and Stephen E. Robertson. “A Probabilistic Model of Information Retrieval: Development and Comparative Experiments (parts 1 and 2).”, Information Processing and Management, 36(6):779-840. 2000, the entire disclosure of which is incorporated herein by reference.

Through a word counting module 2650, word lists for documents and document collections are generated as the words stream therethrough. Associated with each word on the list will be a count value that is incremented each time the associated word is encountered in the stream. FIG. 26(b) depicts an exemplary index 2654 wherein the pointers 106 include a word count parameter we that identifies how many times each term appears in the referenced document. These pointers may also include parameters that identify the size of each document (although this information can be maintained in a separate index table). Table 2654 may also include an additional column 2660 that includes a “collection count” parameter that identifies how many times each term appears in a document collection. Appropriate flags can be added to the bits in the word and position vector stream to identify collection separations. Because the output from the word counting module preferably includes this count information, FIG. 26(a) depicts the output from the word counting module 2650 as including count vectors.

Another metadata generation operation 2104 that can be performed by coprocessor 450 is a classification operation 2600. An exemplary pipeline for such actions is shown in FIG. 26(c). An example of a document classification operation that can be performed on documents includes language classification. With language classification, the document can be applied to a statistical n-gram algorithm that is configured to identify the language that the text within the document most closely matches. See William B. Cavnar and John M. Trenkle, “N-Gram-Based Text Categorization”, Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, pages 161-175, 1994, the entire disclosure of which is incorporated herein by reference. Other document classification operations may employ hidden Markov models (HMMs) to learn some type of classification for the document. See Ludovic Denoyer, Hugo Zaragoza and Patrick Gallinari, “HMM-based Passage Models for Document Classification and Ranking”, Proceedings of ECIR-01, 23rd European Colloquium Information Retrieval Research, Darmstatd, Del., pages 126-135, 2001, the entire disclosure of which is incorporated herein by reference.

FIG. 27(a) depicts an exemplary classification operation wherein a list of words is maintained for a plurality of different classes. For example, a first class 2702 is associated with words 2704 and so on such that class n 2706 is associated with words 2708. These words can be used by one or more matching modules 2300/2310 as keys that are compared against incoming words. As the key words find a match to a word within a document, a scoring memory 2710 that tracks a count of matches for each class can be updated as appropriate. Thus, when the matching module 2300/2310 finds a match between a given word within Document X and a word 2704 within the first class 2702, then the “count” field for class 1 in the scoring memory 2710 associated with that document can be updated. These counts can serve as class scores for each document to thereby indicate how relevant a given document is to a particular class. Preferably, the scoring memory 2710 is refreshed upon encountering a new document in the word stream. Scoring memory 2710 is preferably maintained in the available on-chip memory of the FPGA 402 or in memory device 702 (see FIGS. 7(a) and (b)).

The different classes can pertain to classifications such as “news”, “sports”, “legal”, “medicine”, etc. The words within each class can be words that are generally expected to be found in a document that pertains to the subject defined by the class. For example, the word “baseball” may be present in the word list for a “sports” classification, and the word “estop” may be present in the word list for a “legal” classification. Another exemplary document classification can be language classification. In such an application, each class shown in FIG. 27(a) can be associated with a different language, wherein the words within each class would comprise a full (or redacted) dictionary for that language. With reference to the embodiment of FIG. 24(a), it can be understood that such language classification operations can function as an adjunct to specialized indexing, wherein the matching operations shown in FIG. 24(a) can also operate to update a scoring memory 2710 as appropriate. Yet another exemplary document classification can be “reading comprehension level”. Each class can be associated with a different comprehension level (e.g., “5^thgrade reading level”, “high school freshman reading level”, “undergraduate reading level”, etc.), and the words within each class can be words expected to be encountered in documents belonging to the corresponding comprehension level. It should be noted that the count vector information produced by the word counting module of FIG. 26(a) may be helpful in aiding classifications such as “comprehension level”.

FIG. 27(b) depicts an exemplary classification embodiment wherein decision logic 2712 is employed to assign one or more classes to a document based on the class counts for that document as recorded by scoring memory 2710. For example, each class can have an associated threshold value. If the class count meets or exceeds the threshold value for its class, then the document can be tagged as belonging to that class. As such, it may be possible for the same document to belong to multiple classes should its counts in those classes meet or exceed the classes' assigned threshold values.

Furthermore, it should be noted that one or more regular expression matching modules 2410 can be advantageously employed in connection with classification operation 2600 (possibly in combination with one or more matching modules 2300/2310). For example, if a document is found to contain a credit card number, then an inference could possibly be drawn that the document should be classified as a “sales record”, “personal record”, or the like. Regular expression matching can also be useful to generate counts of how many words have particular character lengths. For example, the regular expression “xxxx” where x can be any character can be used to update a count of 4 letter words, while the regular expression “xxxxx” can be used to update a count of 5 letter words, and so on. Such word length counts can be useful for assessing classifications such as “comprehension level” discussed above.

The classification module 2600 may be used to enrich the word stream emanating from the module 2600 by adding bit strings for each document to the stream, wherein each bit string tags each document as belonging to a particular class. For example, if the classification module is configured to determine which of 20 classifications a document belongs to, a 20 bit string can be added to the output stream for each document, wherein each bit is associated with a particular classification and flags whether the document belongs to that classification. As such, bit X in this bit string can be set aside to flag whether the document contains a credit card number. Thus, if a regular expression matching module 2410 detects the presence of a credit card number pattern within a document, the bit string for that document can be updated to set bit X to high. Similarly, bit Y in the bit string can be set aside to flag whether the document is a legal document. If the classification operation results in a determination that the document should be considered a legal document, then bit Y can be set to high.

It should also be noted that classification operation 2600 can be considered as also belonging to the specialized index generation category of metadata generation operations because the classification information produced by classification operation 2600 can also be used to generate specialized indexes of documents by class. Such indexes can serve as powerful searching aids in that they can be used to answer questions such as “How many Spanish language documents are within the enterprise?” and conduct research such as “What relationships exist between sports and medicine?” and “Find all legal documents in which John Smith is mentioned”.

Also, it should be noted that the classification operation can be performed in software (e.g., software executed by processor 412) using the general and specialized indexes generated from embodiments such as those shown in FIGS. 24(a)-(c). These different indexes can be cross-correlated with pre-defined word lists for the different classes to determine the classification information for each document.

With reference to FIG. 28(a), and following from FIG. 26(a), another metadata generation operation 2104 that can be performed by coprocessor 450 is a histogram generation operation 2800 to thereby aid document clustering. With document clustering, it is desired to know the relevance of different documents to each other. To aid such clustering determinations, the coprocessor 450 can be configured to perform the histogram generation operation 2800 on documents 2100 (wherein this operation can be similar if not identical to the word counting operation described above). These histograms can identify the frequency of words within a document. From the generated histograms, software can be used to perform feature vector expansion and identify document clusters (operation 2802) that are likely related to each other with respect to common subject matter, etc. Feature vector expansion is a standard technique wherein the feature vectors of individual documents are expanded to include words from all the documents in the collection. Clustering can be performed using any of the well-known techniques such as K-means clustering, fuzzy C-means clustering, hierarchical clustering, etc. See M. Steinbach, G. Karypis, and V. Kumar. “A comparison of document clustering techniques.”, KDD Workshop on Text Mining, 2000, the entire disclosure of which is incorporated herein by reference.

Should the stream pre-processing module 2102 already employ word counting, then the histogram generation stage 2800 can be omitted, as shown in FIG. 28(b).

It should be noted that the clustering operations shown by FIGS. 28(a) and (b) can be considered as also belonging to the specialized index generation category of metadata generation operations because the clustering information can also be used to generate specialized indexes of documents by clusters. Such indexes can serve as powerful searching aids in that they can be used to answer questions such as “Which documents within an enterprise are similar to Document X?” and “Which books available for sale from this website are similar to Book X?”.

FIGS. 29(a) and (b) depict exemplary pipelines wherein the hardware-accelerated metadata generation operation 2104 includes classification 2600, specialized index generation 2350, and histogram generation 2800 (or word counting 2650).

FIG. 30 depicts an exemplary environment in which appliance 200 can be effectively employed. FIG. 30 depicts an enterprise computing system 3004 that receives a large volume of incoming data from the Internet 3000. After this incoming data passes enterprise firewall 3002, it can be streamed through appliance 200 which taps into the firewall output. Appliance 200 can thus be used to generate metadata and indexes for the incoming data as that data reaches the enterprise and before it lands in data storage somewhere within enterprise network 420. The data processed by appliance 200 can also include data originating from within the enterprise computing system 3004. Furthermore, appliance 200 can optionally be configured to output its generated metadata for delivery to other repositories within enterprise network 420. Further still, queries can optionally be applied to appliance 200 to conduct full-text searching or other data analysis operations on data indexed by appliance 200. As noted, an example of such a use for appliance 200 is disclosed above and in the above-referenced and incorporated U.S. Pat. No. 7,660,793.

While in the preferred embodiment disclosed herein the coprocessor 450 comprises a reconfigurable logic device 402 such as an FPGA, it should be noted that the coprocessor 450 can be realized using other processing devices. For example, the coprocessor 450 may comprise graphics processor units (GPUs), general purpose graphics processors, chip multi-processors (CMPs), dedicated memory devices, complex programmable logic devices, application specific integrated circuits (ASICs), and other I/O processing components. Moreover, it should be noted that appliance 200 may employ a plurality of coprocessors 450 in either or both of a sequential and a parallel multi-coprocessor architecture.

While the present invention has been described above in relation to its preferred embodiments, various modifications may be made thereto that still fall within the invention's scope. Such modifications to the invention will be recognizable upon review of the teachings herein. Accordingly, the full scope of the present invention is to be defined solely by the appended claims and their legal equivalents.

Claims

1. A method for low latency and high throughput feature vector extraction, the method comprising: receiving streaming unstructured data into a member of the group consisting of (1) a reconfigurable logic device, (2) a graphics processor unit (GPU), and (3) a chip multi-processor (CMP), the streaming unstructured data comprising a plurality of data objects, wherein the data objects include a plurality of words, and wherein the member has a plurality of parallel processing engines deployed thereon;the parallel processing engines analyzing the data objects while the data objects stream through the member to perform a plurality of feature vector extraction operations on the streaming data objects that determine a plurality of features of the streaming data objects, wherein the determined features include a frequency of words within the data objects; andcreating an association that is physically represented in memory between the determined features and the data objects.
2. The method of claim 1 wherein the analyzing step comprises generating a word count for a plurality of the words in the streaming data objects.
3. The method of claim 1 wherein the analyzing step comprises generating histograms with respect to a plurality of the words in the streaming data objects.
4. The method of claim 1 further comprising: performing clustering of the data objects based on the determined features to find clusters of the data objects that share similar features according to clustering criteria.
5. The method of claim 1 wherein the member comprises the reconfigurable logic device.
6. The method of claim 5 wherein the reconfigurable logic device comprises a field programmable gate array (FPGA).
7. The method of claim 6 wherein the FPGA comprises a plurality of FPGAs.
8. The method of claim 7 wherein the parallel processing engines are partitioned across a plurality of the FPGAs.
9. The method of claim 1 wherein the member comprises the GPU.
10. The method of claim 1 wherein the member comprises the CMP.
11. The method of claim 1 further comprising the parallel processing engines creating an index of the streaming data objects based on the determined features.
12. The method of claim 11 wherein the index is stored as structured data in the database, the method further comprising: storing the streaming unstructured data in a data store of unstructured data;receiving a query that is directed toward a combination of structured data and unstructured data;accessing structured data in the database according to the classification index in response to the query to identify a subset of the unstructured data that is to be analyzed against the query; andperforming a query-specified data analysis operation on the identified subset of unstructured data to thereby generate data for a response to the query;wherein the accessing step is conducted by a processor; andwherein the step of performing the query-specified data analysis operation is conducted by the member.
13. An apparatus for low latency and high throughput feature vector extraction, the apparatus comprising: a member of the group consisting of (1) a reconfigurable logic device, (2) a graphics processor unit (GPU), and (3) a chip multi-processor (CMP), the member configured to receive streaming unstructured data, the streaming unstructured data comprising a plurality of data objects, wherein the data objects include a plurality of words, and wherein the member has a plurality of parallel processing engines deployed thereon;the parallel processing engines configured to (1) analyze the data objects while the data objects stream through the member to perform a plurality of feature vector extraction operations on the streaming data objects that determine a plurality of features of the streaming data objects, wherein the determined features include a frequency of words within the data objects, and (2) create an association that is physically represented in memory between the determined features and the data objects.
14. The apparatus of claim 13 wherein the parallel processing engines include a parallel processing engine configured to generate a word count for a plurality of the words in the streaming data objects.
15. The apparatus of claim 13 wherein the parallel processing engines include a parallel processing engine configured to generate histograms with respect to a plurality of the words in the streaming data objects.
16. The apparatus of claim 13 further comprising: a processor configured to cluster the data objects based on the determined features to find clusters of the data objects that share similar features according to clustering criteria.
17. The apparatus of claim 13 wherein the member comprises the reconfigurable logic device.
18. The apparatus of claim 17 wherein the reconfigurable logic device comprises a field programmable gate array (FPGA).
19. The apparatus of claim 18 wherein the FPGA comprises a plurality of FPGAs.
20. The apparatus of claim 19 wherein the parallel processing engines are partitioned across a plurality of the FPGAs.
21. The apparatus of claim 13 wherein the member comprises the GPU.
22. The apparatus of claim 13 wherein the member comprises the CMP.
23. The apparatus of claim 13 wherein the parallel processing engines include a parallel processing engine configured to create an index of the streaming data objects based on the determined features.
24. The apparatus of claim 23 further comprising: a database in which the index is stored;a data store in which the streaming unstructured data is stored; anda processor configured to (1) receive a query that is directed toward a combination of structured data and unstructured data and (2) access structured data in the database according to the index in response to the query to identify a subset of the unstructured data that is to be analyzed against the query; andwherein the member is configured to perform a query-specified data analysis operation on the identified subset of unstructured data to thereby generate data for a response to the query.

CROSS-REFERENCE AND PRIORITY CLAIM TO RELATED PATENT APPLICATIONS

This patent application is a continuation of U.S. patent application Ser. No. 15/211,864, filed Jul. 15, 2016, now U.S. Pat. No. 10,191,974, which is a continuation of U.S. patent application Ser. No. 14/531,255, filed Nov. 3, 2014, now U.S. Pat. No. 9,396,222, which is a continuation of U.S. patent application Ser. No. 13/442,442, filed Apr. 9, 2012, now U.S. Pat. No. 8,880,501, which is a continuation of U.S. patent application Ser. No. 12/640,891, filed Dec. 17, 2009, now U.S. Pat. No. 8,156,101, which is a continuation of U.S. patent application Ser. No. 11/938,709, filed Nov. 12, 2007, now U.S. Pat. No. 7,660,793, which claims priority to U.S. provisional patent application 60/865,629 filed Nov. 13, 2006, the entire disclosures of each of which are incorporated herein by reference. This patent application is related to U.S. patent application Ser. No. 11/938,732, filed Nov. 12, 2007, entitled “Method and System for High Performance Data Metatagging and Data Indexing Using Coprocessors”, and published as U.S. Patent Application Publication 2008/0114725 A1, the entire disclosure of which is incorporated herein by reference. This patent application is also related to U.S. patent application Ser. No. 13/686,338, filed Nov. 27, 2012, now U.S. Pat. No. 9,323,794, which is a continuation of the above-referenced '732 patent application.

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Related Publications (1)

	Number	Date	Country
	20190155831 A1	May 2019	US

Provisional Applications (1)

	Number	Date	Country
	60865629	Nov 2006	US

Continuations (5)

	Number	Date	Country
Parent	15211864	Jul 2016	US
Child	16259326		US
Parent	14531255	Nov 2014	US
Child	15211864		US
Parent	13442442	Apr 2012	US
Child	14531255		US
Parent	12640891	Dec 2009	US
Child	13442442		US
Parent	11938709	Nov 2007	US
Child	12640891		US

Method and system for high performance integration, processing and searching of structured and unstructured data

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