Abstract: An automation analytics system and method for building analytical models for an education application uses data-availability segments of students, which are clustered into segment clusters, to create the analytical models for the segment clusters using a machine learning process. The analytical models can be used to identify at least at least actionable insights.

Abstract: A flexible persistence modeling system and method for building flexible persistence models for education institutions using a Markov model based on units of academic progress of a non-traditional learning program of an education institution. The Markov model is used to quantify transitions of students between the states as parameters of state transitions so that features from the Markov model with the parameters of state transitions can be extracted that are related to the non-traditional learning program of the education institution using defined flexible persistence. The extracted features can then be used to build at least one flexible persistence model for different segments of the students.

Abstract: A flexible persistence modeling system and method for building flexible persistence models for education institutions using a Markov model based on units of academic progress of a non-traditional learning program of an education institution. The Markov model is used to quantify transitions of students between the states as parameters of state transitions so that features from the Markov model with the parameters of state transitions can be extracted that are related to the non-traditional learning program of the education institution using defined flexible persistence. The extracted features can then be used to build at least one flexible persistence model for different segments of the students.

Abstract: A flexible persistence modeling system and method for building flexible persistence models for education institutions using a Markov model based on units of academic progress of a non-traditional learning program of an education institution. The Markov model is used to quantify transitions of students between the states as parameters of state transitions so that features from the Markov model with the parameters of state transitions can be extracted that are related to the non-traditional learning program of the education institution using defined flexible persistence. The extracted features can then be used to build at least one flexible persistence model for different segments of the students.

Abstract: An automation analytics system and method for building analytical models for an education application uses data-availability segments of students, which are clustered into segment clusters, to create the analytical models for the segment clusters using a machine learning process. The analytical models can be used to identify at least at least actionable insights.

Abstract: The hierarchical relationships between objects in different levels of an object structure (such as a contract) are stored as elements in two-dimensional matrix representations. In general, the matrix representations facilitate queries, clustering of like objects and contracts, and comparisons that identify common objects and contracts.

Abstract: The hierarchical relationships between objects in different levels of an object structure (such as a contract) are stored as elements in two-dimensional matrix representations. In general, the matrix representations facilitate queries, clustering of like objects and contracts, and comparisons that identify common objects and contracts.

Abstract: A system and method for providing to an individual certain information which is relevant to that individual's health status. More specifically, the invention relates to a system and method for providing information relevant to an individual's health status and wherein the information provided is chosen on the basis of not only the individual's health status but also complex psychosocial behavioral factors related to that individual's motivational structure and likely impact.

Abstract: The present invention relates to a system and method for assessing individual healthfulness and for providing health-enhancing behavioral advice and promoting adherence thereto. More specifically, the invention relates to a system and method for eliciting one or more responses from an individual pertaining to the individual's health; for assessing, based on the responses, the individual's health; and for providing the individual advice concerning behavior to enhance the individual's health, and in which the advice is provided in a form and manner promoting adherence thereto.

Abstract: The present invention relates to a system and method for automated extraction and display of past health care use to aid in predicting future health status. A system and method that converts raw medical and pharmacy claims data into Hierarchical Major Clinical Condition (HMCC) and Place of Treatment (POT) time-series data to facilitate the health assessment of a member's total clinical conditions and aid in predicting his or her future health status. The HMCC categories are organized in body systems and likely disease progression to permit both spatio-temporal digital signal processing and the development of a dynamical learning system. Each medical and pharmacy claim of the member is mapped onto one or more HMCC/POT-time cells. At the end of mapping, multiple entries in each HMCC-time cell are accumulated with the temporal resolution determined as a function of group size and temporal fidelity required for model building.

Abstract: A system and method that generates a hierarchical tree network and uses linear-plus-nonlinear learning algorithms to form a consensus view on a member's future health status. Each leaf in the hierarchical tree network is homogeneous in clinical characteristics, experience period, and available data assets. Optimization is performed on each leaf so that features and learning algorithms can be tailored to local characteristics specific to each leaf.

Abstract: A system and method for estimating the point of diminishing returns for additional information in data mining processing applications. The present invention provides a convenient method of estimating the extent to which a data mining algorithm captures useful information in raw feature data. First, the input data is processed using a forward transform. A region of overlap Yo in the forward transformed data is identified and quantified. The region of overlap Yo is processed with a reverse transform to create an overlap region Z in an original feature space. The degree of overlap in region Z is quantified and compared to a level of overlap in the Yo region, such that the comparison quantifies the extent to which a data mining algorithm captures useful information in the input data.

Abstract: A method and apparatus in various embodiments for controlling a data mining operation by specifying the goal of data mining in natural language, processing the data mining operation without any further input beyond the problem specification, and displaying key performance results of a data mining operation in natural language. One embodiment includes provides a user interface having a control for receiving natural language input describing the goal of the data mining operation from the control on the user interface. A second embodiment identifies key performance results, providing a user interface having a control for communicating information, and communicating a natural language description of the key performance results using the control on the user interface. In a third embodiment input data determining a data mining operation goal is the only input required by the data mining application.

Abstract: One embodiment is a method to identify a preprocessing algorithm for raw data. The method may includes the steps of providing an algorithm knowledge database including preprocessing algorithm data and feature set data associated with the preprocessing algorithm data, analyzing raw data to produce analyzed data, extracting from the analyzed data features that characterize the data, and selecting a preprocessing algorithm using the algorithm knowledge database and features extracted from the analyzed data. Another embodiment is a data mining system for identifying a preprocessing algorithm for raw data using this method.

Abstract: A training database (including data mining algorithm descriptions and metafeatures characterizing probability density functions of features) in the memory and computer readable program code (i) to extract features that classify data, (ii) to calculate metafeatures describing the case probability density function, and (iii) to select a data mining algorithm by using the training database to map the calculated metafeatures describing the case probability density function to the selected data mining algorithm. The frequency of the occurrence of features with respect to datum in the data defining a case probability density function.

Abstract: An automatic data mining tool that characterizes the relationships between different database fields from both structured and unstructured data. It extracts a data model, identifies and categorizes all the data fields, performs pre-processing to deal with unstructured data effectively, and processes the data without human intervention to automatically explore how the fields are related to one another. Prior to the commencement of user-controlled data mining, the present invention goes through all the fields in a database table space in order to establish meaningful relationships between various fields using whatever computer resources are available (i.e. by using “cycle stealing”). This allows the present invention to run in the background and establish relationships between fields even before data mining (DM) begins, and determine redundant, useless, and/or trivial fields without any external guidance.

Abstract: Various modes and embodiment of a method, apparatus, user interface, article of manufacture including a computer readable medium, computer data signals embodied on a carrier wave, and computer system for a GUI-based ground truth tool and insertion of user algorithms written in multiple programming languages. One embodiment comprises user interface for inserting a custom algorithm in a data-mining application. Another embodiment comprises a ground truth tool in a data-mining-application. A third embodiment comprises seamless insertion of custom algorithms in a data-mining application using tap points.

Abstract: A method, system, and article of manufacture for transforming a relational database having a hierarchical parent-child structure into a flat database more amendable to conventional data mining techniques. Generate a hierarchical data tree based on a relational data model. Perform a bottom-up summarization starting from the children and proceeding to the next higher level. Append to parent records new fields summarizing the contents of child records associated therewith. Also a computer system comprising means for performing these functions.

Abstract: A system and method for estimating the point of diminishing returns for additional information in data mining processing applications. The present invention provides a convenient method of estimating the extent to which a data mining algorithm captures useful information in raw feature data. First, the input data is processed using a forward transform. A region of overlap Yo in the forward transformed data is identified and quantified. The region of overlap Yo is processed with a reverse transform to create an overlap region Z in an original feature space. The degree of overlap in region Z is quantified and compared to a level of overlap in the Yo region, such that the comparison quantifies the extent to which a data mining algorithm captures useful information in the input data.