Abstract: A management server measures network activity of user devices to determine activities of the users associated with each user device. The management server generates digital model personas corresponding to the users based on one or more activities of the user. The management server clusters the digital model personas to generate user groups based on similar activities, and compares a first digital model persona from a first user with at least one second digital model persona.

Abstract: A network management system tests the availability of a network resource before a user performs a task with the network resource. The system measures network activity of a user performing one or more tasks. The network activity includes communication between a user device of the user and each network resource associated with a corresponding task performed by the user. The system also generates a digital model persona of the user based on the tasks performed by the user, and determines a schedule of the tasks performed the user. Each particular task is associated with a corresponding execution time for the user. The system further configures the digital model persona to test the network resource associated with each corresponding task at a testing time that is a predetermined length of time prior to the execution time for the user.

Abstract: Systems and methods that cleanse data in Extract, Transform, Load environments (ETL), via employing an outlier detect component that is positioned in data pipeline to data warehouse(s). Such outlier detect component employs a cluster mining model to split data into normal and outlier data. Different predictive models can be employed to detect outliers in different data slices to enhance the accuracy of the predictions. In addition, a graphical user interface (GUI) enables a user to interact with cluster groups that are created and/or analyzed by the outlier detect component.

Abstract: Architecture for analyzing pattern shifts in data patterns of data mining models and outputting the results. This allows comparing and describing differences between two semantically similar sets of patterns (or mining models), and for analyzing historical changes in versions of the same model or differences in patterns found by two or more different algorithms applied to the same data. The architecture can also facilitate explaining data patterns that shift over time and over different data populations, and between versions of the same model that use different algorithms. A model component is employed for storing data mining models have respective sets of data patterns obtained from a dataset, and an analysis component analyzes the sets of the data patterns for difference data therebetween. The dataset can be a subsample of a larger set of data and can be analyzed by the analysis component over a time period.

Abstract: A realtime training model update architecture for data mining models. The architecture facilitates automatic update processes with respect to evolving source/training data. Additionally, model update training can be performed at times other than in realtime. Scheduling can be invoked, for periodic and incremental updates, and refresh intervals applied through the training parameters for the mining structure and/or model. Training can also be triggered by user-defined events such as database notifications, and/or alerts from other operational systems. In support thereof, a data mining model component is provided for training a data mining model on a dataset in realtime, and an update component for incrementally training the data mining model according to predetermined criteria. Additionally, model versioning and version comparison can be employed to detect significant changes and retain updated models. Training data aging/weighting of training data can be applied.

Abstract: 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.

Abstract: A unique system and method that facilitates diagramming data mining output to create an interactive rendering of the output are provided. The system and method involve a diagramming system that includes various data mining templates such as decision tree and dependency network templates. When a template is dragged to a work space in the diagramming system, selection of a data source, a model, and one or more rendering options can be made before the model is rendered. The model is interactive, thus it can be modified and annotated apart or separate from the context of the data mining engine or viewer. As a result, users can more readily incorporate such rendered models into other applications such as presentations and can continue to interact with them. Examples of interactions include changing node color, content, connection points, page location, size or shape, and shading and performing tree operations or dependency net operations.

Abstract: The subject disclosure pertains to systems and methods for data caching and/or lookup. A data-mining model can be employed to identify data item relationships, associations, and/or affinities. A cache or other fast memory can then be populated based on data mining information. A lookup component can interact with the memory to facilitate expeditious lookup or discovery of information, for example to aid data warehouse population, amongst other things.

Abstract: The subject invention leverages scaleable itemsets and/or association rules to provide dynamic adjustment of memory usage. This allows the subject invention to provide association rules and/or itemsets with the highest support while utilizing a bounded amount of memory. Thus, a data analysis system and/or method utilizing the subject invention can self-adjust to provide the best association rules and/or itemsets based on available system resources. One instance of the subject invention employs dynamically adjustable minimum support values for data itemsets and/or association rules to facilitate in compensating for memory availability. In yet another instance of the subject invention a prefix tree data structure is utilized to facilitate in constructing itemsets. Memory utilization is then adjusted via pruning and/or reallocation of counter vectors and/or pointer vectors and/or reallocation of nodes of the prefix tree data structure for scaleable data itemsets and/or association rules.

Abstract: The subject invention relates to systems and methods that extend the network data access capabilities of mark-up language protocols. In one aspect, a network data transfer system is provided. The system includes a protocol component that employs a computerized mark-up language to facilitate data interactions between network components, whereby the data interactions were previously limited or based on a statement command associated with the markup language. An extension component operates with the protocol component to support the data transactions, where the extension component supplies at least one other command from the statement command to facilitate the data interactions.

Abstract: The subject disclosure pertains to extensible data mining systems, means, and methodologies. For example, a data mining system is disclosed that supports plug-in or integration of non-native mining algorithms, perhaps provided by third parties, such that they function the same as built-in algorithms. Furthermore, non-native data mining viewers may also be seamlessly integrated into the system for displaying the results of one or more algorithms including those provided by third parties as well as those built-in. Still further yet, support is provided for extending data mining languages to include user-defined functions (UDFs).

Abstract: Architecture that facilitates syntax processing for data mining statements. The system includes a syntax engine that receives as an input a query statement which, for example, is a data mining request. The statement can be generated from many different sources, e.g., a client application and/or a server application, and requests query processing of a data source (e.g., a relational database) to return a result set. The syntax engine includes a binding component that converts the query statement into an encapsulated statement in accordance with a predefined grammar. The encapsulated statement includes both data and data operations to be performed on the data of the data source, and which is understood by the data source. An execution component processes the encapsulated statement against the data source to return the desired result set.

Abstract: A system that facilitates data mining comprises a reception component that receives command(s) in a declarative language that relate to utilizing an output of a first data mining model as an input to a second data mining model. An implementation component analyzes the received command(s) and implements the command(s) with respect to the first and second data mining models. In another aspect of the subject invention, the reception component can receive further command(s) in a declarative language with respect to causing one or more of the first and second data mining models to output a prediction, the prediction desirably generated without prediction input, the implementation component causes the one or more of the first and second data mining models to output the prediction.

Abstract: The subject invention relates to systems and methods to extend the capabilities of declarative data modeling languages. In one aspect, a declarative data modeling language system is provided. The system includes a data modeling language component that generates one or more data mining models to extract predictive information from local or remote databases. A language extension component facilitates modeling capability in the data modeling language by providing a data sequence model or a time series model within the data modeling language to support various data mining applications.

Abstract: A language schema that integrates multidimensional extensions (e.g., MDX) and data mining extensions (e.g., DMX) for performing data mining operations on data residing in OLAP cubes. The schema provides that the <source-data-query> can not only be a relational query, rather a multidimensional query formed using MDX, for example. The operations of model creation, training and prediction are described.

Abstract: A method for performing data mining is provided. The method includes selecting at least one data source of unstructured text. Additionally, a transformation is selected to identify a list of terms in the unstructured text. A run-time path is established to connect the data source to the transformation to load the list of terms identified into a destination database.

Abstract: A mining structure is created which contains processed data from a data set. This data may be used to train one or more models. In addition to the selection of data to be used by model from data set, processing parameters are set, in one embodiment. For example, the discretization of a continuous variable into buckets, the number of buckets, and/or the sub-range corresponding to each bucket is set when the mining structure is created. The mining structure is processed, which causes the processing and storage of data from data set in the mining structure. After processing, the mining structure can be used by one or more models.

Abstract: A drill-through feature is provided which provides a universal drill-through to mining model source data from a trained mining model. In order for a user or application to obtain model content information on a given node of a model, a universal function is provided whereby the user specifies the node for a model and data set, and the cases underlying that node for that model and data set are returned. A sampling of underlying cases may be provided, where only a sampling of the cases represented in the node is requested.