Unstructured data in a mining model language

A standard mechanism for directly accessing unstructured data types (e.g., image, audio, video, gene sequencing and text data) in accordance with data mining operations is provided. The subject innovation can enable access to unstructured data directly from within the data mining engine or tool. Accordingly, the innovation enables multiple vendors to provide algorithms for mining unstructured data on a data mining platform (e.g., an SQL-brand server), thereby increasing adoption. As well, the subject innovation allows users to directly mine unstructured data that is not fixed-length, without pre-processing and tokenizing the data external to the data mining engine. In accordance therewith, the innovation can provide a mechanism to expand declarative language content types to include an “unstructured” data type thereby enabling a user and/or application to affirmatively designate mining data as an unstructured type.

BACKGROUND

Computers and computer-based devices have become a necessary tool for many applications throughout the world. Typewriters and slide rules have become obsolete in light of keyboards coupled with sophisticated word-processing applications and calculators that include advanced mathematical functions/capabilities. Thus, trending applications, analysis applications, and other applications that previously may have required a collection of mathematicians or other high-priced specialists to painstakingly complete by hand can now be accomplished through use of computer technology. For instance, due to ever-increasing processor and memory capabilities, if data is entered properly into an application/wizard, such application/wizard can automatically output a response nearly instantaneously (in comparison to hours or days generating such response by hand previously required).

Furthermore, through utilization of computers and computer-related devices, vast magnitudes of data can be obtained for analysis and predictive purposes. For example, a retail sales establishment can employ a data analysis application to track sales of a particular good given a particular type of customer, income level of customers, a time of year, advertising strategy, and the like. More particularly, patterns within collected structured data can be determined and analyzed, and predictions relating to future events can be generated based upon these patterns. While the above example describes utilizing data in connection with retail sales, it is understood that various applications and contexts can benefit from analysis of accumulated data.

The aforementioned analysis of data, recognition of patterns, and generation of predictions based at least in part upon the recognized patterns can be collectively referred to as data mining. Conventionally, to enable suitable data mining, various models must be programmed and trained by way of training data. For instance, data previously collected can be employed as training data for one or more data mining models. The data mining models can employ various decision tree structures to assist in generating predictions, and can further utilize suitable clustering algorithms to cluster data analyzed by the data mining models. Accordingly, these data mining models can be extremely complex and require significant programming from an expert computer programmer.

Due to complexity of data mining models and extensiveness of computations utilized in connection with such data mining models, there currently exist various deficiencies associated therewith. For example, conventional systems are primarily directed to mining structured (e.g., relational) data. However, unstructured data is oftentimes available in the form of text documents, for example, related to Internet web pages and the like. Similarly, unstructured data can refer to image data as well as streaming audio and/or video. Today, developments in the field of mining unstructured data require extensive pre-processing and/or tokenizing of the data external to the data mining tool.

SUMMARY

The innovation disclosed and claimed herein, in one aspect thereof, comprises a standard mechanism for accessing and processing of unstructured data types in data mining. More particularly, the subject innovation enables access to unstructured data directly from within the data mining engine or tool. Accordingly, in one particular aspect, the innovation enables multiple vendors to provide algorithms for mining unstructured data (e.g., image, audio, video, gene sequencing and text data) on a data mining platform (e.g., an SQL-brand server data mining platform), thereby increasing adoption.

In another aspect of the subject innovation, the invention allows users to directly mine unstructured data (e.g., including image, audio, video, gene sequencing and text data) that are not fixed-length, without pre-processing and tokenizing the data outside the data mining engine. In accordance therewith, the innovation can provide a mechanism to expand declarative language content types to include an “unstructured” data type thereby enabling a user and/or application to affirmatively designate mining data as an “unstructured” type.

In another aspect, a receiving component and an unstructured data processing component can be employed to receive and process the declarative language syntax respectively. The unstructured data processing component can facilitate extracting key portions of the unstructured data, analyzing the extracted portions and converting the extracted portions into a format recognizable to a mining algorithm.

In still other aspects, the invention can include syntax for building mining models on unstructured data types. More particularly, the syntax can be an extension to both the DMX language and as well the XML DDL that can be used for defining data mining objects. More particularly, the syntax can include extending the list of supported content types to include an unstructured keyword, for example, “UNSTRUCTURED.” Other aspects can employ alternative keywords without departing from the spirit and scope of the innovation. As well, an aspect can include a mechanism for processing, storing and passing unstructured data to native (built-in) and plug-in algorithms. This includes a mechanism for algorithms to access both the tokenized and raw values of attribute states.

Still further, aspects can include syntax for term extraction during querying using the DMX language. Other aspects can include syntax for performing term extraction and lookup through an SQL UDF (user-defined function) that can then be used within the source query for the input rowset in a DMX prediction join.

In yet another aspect thereof, an artificial intelligence component is provided that employs a probabilistic and/or statistical-based analysis to prognose or infer an action that a user desires to be automatically performed.

DETAILED DESCRIPTION

Referring initially to the drawings,FIG. 1illustrates a system100that facilitates directly mining unstructured data in accordance with an aspect of the innovation. Generally, system100can include a data mining tool102that can directly mine data maintained within a data source (or group of data sources)104. As will be understood upon a review of the figures that follow, the novel data mining tool (e.g., engine)102can be employed to mine any type of unstructured data including, but not limited to, text documents, web pages, image data, audio data, streaming video, streaming audio, gene sequencing data or the like. Essentially, any unstructured data can be directly mined by the subject novel innovation.

As shown inFIG. 1, the novel data mining tool102can include a receiving component106and an unstructured data processing component108. In operation, the receiving component106can accept a command or instruction that identifies data as an unstructured type. In a disparate aspect, this command can be received from a user or a computer-implemented component. In one particular example, the novel innovation can expand the data mining extension (DMX) query language (or any other suitable declarative language) to include a type “unstructured” thereby enabling a user to prompt a data mining operation upon unstructured data. It will be understood and appreciated that the DMX query language is a query language related to building, managing and working with data mining models. In other words, DMX can be employed to create the structure of data mining models, train the models as well as to browse, manage and predict against the models. Further, it is to be understood that, although the keyword “unstructured” is used in the examples herein, any keyword designator can be employed to identify unstructured data without departing from the spirit and scope of the subject invention.

In accordance therewith, the unstructured data processing component108can be employed to identify key terms or portions of the unstructured data. For example, suppose the unstructured data is a text document, the unstructured data processing component108can be employed to locate key terms within the document. Once identified, the unstructured data component108can convert the unstructured data into a format consistent with a desired data mining algorithm(s). Moreover, the converted unstructured data can be passed to the data mining model algorithm.

At202, the unstructured data can be identified within the declarative language. For example, the conventional DMX language can be expanded to include a data type of “unstructured” whereby a user (or application) can identify data as such. At204, key terms/phrases (or other portions of data) can be extracted from the unstructured data. These extracted key terms/phrases or other portions thereof can be analyzed at206.

At208, the extracted information can be converted into a form recognizable to an appropriate algorithm. For example, the information can be converted into tokens and raw values consistent with a particular modeling algorithm. In accordance therewith, the information can be passed along to the modeling algorithm. Finally, a stop block is reached.

Turning now toFIG. 3, block diagram of an unstructured data processing component108in accordance with an aspect of the innovation is shown. More particularly, the unstructured data processing component108includes an extraction component302, an analysis component304and a data conversion component306. The functionality of each of these components will be described in greater detail infra.

As described above, the extraction component302can be employed to extract key terms/phrases from an unstructured text document. In another example, the extraction component302can be employed to identify key portions of image data, audio data, video data, gene sequences, etc. In any case, it is to be understood that the extraction component302can be employed to locate and extract key elements from within the unstructured data.

Typically in data mining, the way that data mining looks at data is by organizing the data into attributes, for example, discrete or continuous attributes. In most conventional cases, the data is structured data such that the attributes can be easily converted into a numerical value with an associated state. Accordingly, the data mining algorithm can analyze this information.

In contrast with conventional systems, the subject innovation can be employed to directly mine unstructured data. As described above, the innovation can facilitate direct mining of unstructured data including, but not limited to, text documents, video streams, audio streams, gene sequencing data, etc. As will be understood, unstructured data is information without specific definitions with regard to type. It will further be understood that algorithms exist that can determine patterns with regard to these types of data streams. However, the subject innovation can employ the analysis component304and the data conversion component306to process (or pre-process) the information such that the algorithm(s) can recognize the data. This pre-processing phase can be effectuated directly within the data mining engine (e.g.,102ofFIG. 1).

One novel feature of the subject innovation is that the subject innovation can expand the scope of data mining beyond structured or relational data to include analyzing data and patterns in unstructured data. The following scenario is provided to add context to the innovation and is not intended to limit the innovation in any way. As such, it will be understood that an unlimited number of applications exist where unstructured data can be mined in order to identify patterns within the data. As such, these additional scenarios and application are to be included within the scope of this innovation and claims appended hereto.

Referring now to a specific scenario, the subject innovation can be employed in connection with webpage feedback where users enter unstructured feedback with respect to a web page. As such, it can be desirable to look at the feedback, analyze it and route it to the appropriate people to handle such requests, problems or concerns. In accordance therewith, it is important to extract the key terms and phrases from the document and convert the information into a format that the data mining algorithm will understand. As described with respect toFIG. 3, these actions can be effectuated via the unstructured data processing component108and more particularly, via the extraction component302, the analysis component304and the data conversion component306.

In other words, as described with reference to conventional systems, one phase of dealing with unstructured data is the pre-processing step. It is a novel feature of the subject innovation to directly provide this pre-processing phase within the data mining tool or engine (e.g., unstructured data processing component108). In other words, in this pre-processing step, the unstructured data can ultimately be converted (via data conversion component306) into a set of attributes and values that can be recognized by a data mining algorithm. Subsequently, this information can be fed into a data mining algorithm.

Turning now toFIG. 4, an alternative architectural diagram of system100is shown in accordance with a disparate aspect of the innovation. More specifically, system100illustrated inFIG. 4includes an interface component402that facilitates passing the formatted data to a native (or built-in) algorithm component404. As described supra, the formatted data is representative of the key portions of the unstructured data that have been converted into a format compatible with native algorithm component404.

As shown inFIG. 5, it is another novel feature of the innovation to enable the use of remote and/or plug-in algorithm components502. As shown, the interface component402can be employed to pass the formatted data to a plug-in algorithm component502. This feature can be particularly useful to enable use of and compatibility with third-party algorithms.

All in all, aspects of the subject innovation can enable users to directly mine unstructured data within the data mining tool or engine rather than relying on a pre-processing phase or external components to process and prepare the unstructured data to be used in data mining model generation. In other words, the system100can enable a user to mine data regardless of whether the data is structured, unstructured or a combination thereof.

It will be understood that, as part of the definition of the mining model, there can be different columns of data. Accordingly, the content type (e.g., integer, text) can be identified with respect to each column. By way of more specific example, suppose the columns were age and gender, these are the attributes. Age can be identified as content type integer and continuous where gender can be classified as discrete of content type text.

In accordance with the subject innovation, there can be a field that contains the content of the document or a pointer to the document and the type can be designated as “unstructured.” This designation can be passed to the receiving component106by way of a data declaration language (e.g., DMX). In other words, the user can let the data mining engine102know that the field contains unstructured data whereby an appropriate data mining algorithm (404,502) can be employed to process or mine the data.

More particularly, the acts that make up the pre-processing phase can occur directly within the engine in accordance with the subject innovation. This novel feature can be effected by expanding the data declaration language definitions to include an “unstructured” designation content type.

It is to be understood that the processing of the unstructured data within the data mining engine can occur in a variety of ways. More particularly, any number of algorithms (e.g., native or third-party plug-in) can be employed to process the unstructured data directly within the mining engine or tool. To this end, the innovation can model unstructured data and provide the infrastructure to process the unstructured data thereafter passing the data to an appropriate algorithm inside of the data mining engine.

As described supra, conventionally, there is no mechanism available to employ a third party plug-in algorithm that can process unstructured data. This is mainly because, conventionally, the algorithm is not supported by the engine. In other words, a mechanism does not exist to pass the data to the algorithm directly from within the mining engine.

In summary, the innovation enables users to directly mine unstructured data (including text, image, audio, video, gene sequencing data, etc.) that are not fixed-length, without pre-processing and tokenizing the data outside the data mining engine. As described supra and with continued reference toFIG. 5, in one aspect, the innovation includes data declaration language syntax for building mining models on unstructured data types. In a particular aspect, the syntax can represent an extension to both the DMX language. As well, in an aspect, the XML DDL that can be used for defining data mining objects. Essentially, in an aspect, the syntax extends the list of supported content types to include the keyword “UNSTRUCTURED.” This or any other desired keyword can be employed to inform the data mining tool of an unstructured data type.

It will be appreciated that, in accordance with aspects, data can have mixed content types (e.g., structured and unstructured) with respect to the data mining operation. From the language perspective, content type identification can be particularly easy because of the presence of the declarative language content types for identifying what the content actually represents. In addition to the data type (e.g., integer, text), the subject innovation facilitates adding a new content type related to “unstructured” data. As described above, the unstructured data can be some type of stream data, for example, it can be a text document, a video stream, an audio stream, a gene sequence, etc.

As shown inFIG. 5, the data mining tool102can include an unstructured data processing component108that provides a mechanism for processing, storing and passing unstructured data to native (e.g., built-in) and/or plug-in (e.g., third-party) algorithms. Additionally, the data mining tool102can include a mechanism (e.g., interface component402) that enables algorithms to access both the tokenized and raw values of attribute states.

As described above, the receiving component106can receive and/or access a command, or set of commands, written in a declarative data language. This command(s) can employ the expanded data content type “unstructured” to advise the data mining engine102of the unstructured data type. As well, the declarative data language (e.g., DMX) can include syntax for term extraction during querying using the DMX language.

As described herein, the subject novel innovation provides a mechanism to read and store unstructured data and a way to pass the data to resident algorithms as well as plug-in algorithms. In accordance therewith, the subject innovation can read data streams and convert them to a standard format that can be understood by an algorithm or group of algorithms. As such, the innovation enables access to the raw value for an attribute state.

Continuing with the aforementioned example, suppose the attribute is gender, the innovation enables this attribute to be conveyed as a zero or a one corresponding to male and female respectively or vice-versa. Currently, there is not a mechanism for algorithms to access raw states. This is particularly useful where the algorithm has to derive semantic information from the raw value of states. Therefore, the subject innovation includes an interface (e.g.,402ofFIG. 4) that enables the algorithms to access these raw values.

Following is an exemplary syntax for evaluating documents and extracting key terms or portions thereof. It is to be understood that, conventionally, this extraction process was previously included in the pre-processing step external to the data mining engine. The subject innovation can build this novel functionality into the language since the raw values and the mechanism(s) are resident within the engine. Therefore, the innovation can expose a mechanism that users can extract these key terms and query upon them from the language.

Below is an exemplary syntax that effects term extraction in accordance with an aspect of the innovation.

Similarly, in a disparate aspect, the system can employ particular syntax for performing term extraction and lookup through an SQL UDF (user-defined function). In operation, these terms can be used within a source query for the input rowset in a DMX prediction join, for example,

Another aspect of the innovation can employ an artificial intelligence (AI) component which facilitates automating one or more features in accordance with the subject innovation. By way of example, the subject innovation (e.g., in connection with identification/extraction of key terms, algorithm selection) can employ various AI-based schemes for carrying out various aspects thereof.

A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be automatically performed. In the case of database systems, for example, attributes can be words or phrases or other data-specific attributes derived from the words (e.g., database tables, the presence of key terms), and the classes can be categories or areas of interest (e.g., levels of priorities).

As will be readily appreciated from the subject specification, the subject innovation can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via observing user behavior, receiving extrinsic information). For example, SVM's are configured via a learning or training phase within a classifier constructor and feature selection module. Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining according to a predetermined criteria which key terms, phrases or portions of the unstructured data should be identified and/or extracted.

Referring now toFIG. 6, there is illustrated a block diagram of a computer operable to execute the disclosed architecture of directly mining unstructured data. In order to provide additional context for various aspects of the subject innovation,FIG. 6and the following discussion are intended to provide a brief, general description of a suitable computing environment600in which the various aspects of the innovation can be implemented. While the innovation has been described above in the general context of computer-executable instructions that may run on one or more computers, those skilled in the art will recognize that the innovation also can be implemented in combination with other program modules and/or as a combination of hardware and software.

With reference again toFIG. 6, the exemplary environment600for implementing various aspects of the innovation includes a computer602, the computer602including a processing unit604, a system memory606and a system bus608. The system bus608couples system components including, but not limited to, the system memory606to the processing unit604. The processing unit604can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures may also be employed as the processing unit604.

The system bus608can be any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory606includes read-only memory (ROM)610and random access memory (RAM)612. A basic input/output system (BIOS) is stored in a non-volatile memory610such as ROM, EPROM, EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer602, such as during start-up. The RAM612can also include a high-speed RAM such as static RAM for caching data.

The computer602further includes an internal hard disk drive (HDD)614(e.g., EIDE, SATA), which internal hard disk drive614may also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD)616, (e.g., to read from or write to a removable diskette618) and an optical disk drive620, (e.g., reading a CD-ROM disk622or, to read from or write to other high capacity optical media such as the DVD). The hard disk drive614, magnetic disk drive616and optical disk drive620can be connected to the system bus608by a hard disk drive interface624, a magnetic disk drive interface626and an optical drive interface628, respectively. The interface624for external drive implementations includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies. Other external drive connection technologies are within contemplation of the subject innovation.

A number of program modules can be stored in the drives and RAM612, including an operating system630, one or more application programs632, other program modules634and program data636. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM612. It is appreciated that the innovation can be implemented with various commercially available operating systems or combinations of operating systems.

A user can enter commands and information into the computer602through one or more wired/wireless input devices, e.g., a keyboard638and a pointing device, such as a mouse640. Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, touch screen, or the like. These and other input devices are often connected to the processing unit604through an input device interface642that is coupled to the system bus608, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc.

A monitor644or other type of display device is also connected to the system bus608via an interface, such as a video adapter646. In addition to the monitor644, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

The computer602may operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s)648. The remote computer(s)648can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer602, although, for purposes of brevity, only a memory/storage device650is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN)652and/or larger networks, e.g., a wide area network (WAN)654. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer602is connected to the local network652through a wired and/or wireless communication network interface or adapter656. The adapter656may facilitate wired or wireless communication to the LAN652, which may also include a wireless access point disposed thereon for communicating with the wireless adapter656.

When used in a WAN networking environment, the computer602can include a modem658, or is connected to a communications server on the WAN654, or has other means for establishing communications over the WAN654, such as by way of the Internet. The modem658, which can be internal or external and a wired or wireless device, is connected to the system bus608via the serial port interface642. In a networked environment, program modules depicted relative to the computer602, or portions thereof, can be stored in the remote memory/storage device650. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

Referring now toFIG. 7, there is illustrated a schematic block diagram of an exemplary computing environment700in accordance with the subject innovation. The system700includes one or more client(s)702. The client(s)702can be hardware and/or software (e.g., threads, processes, computing devices). The client(s)702can house cookie(s) and/or associated contextual information by employing the innovation, for example.

The system700also includes one or more server(s)704. The server(s)704can also be hardware and/or software (e.g., threads, processes, computing devices). The servers704can house threads to perform transformations by employing the innovation, for example. One possible communication between a client702and a server704can be in the form of a data packet adapted to be transmitted between two or more computer processes. The data packet may include a cookie and/or associated contextual information, for example. The system700includes a communication framework706(e.g., a global communication network such as the Internet) that can be employed to facilitate communications between the client(s)702and the server(s)704.

Communications can be facilitated via a wired (including optical fiber) and/or wireless technology. The client(s)702are operatively connected to one or more client data store(s)708that can be employed to store information local to the client(s)702(e.g., cookie(s) and/or associated contextual information). Similarly, the server(s)704are operatively connected to one or more server data store(s)710that can be employed to store information local to the servers704.