Abstract: Data management techniques are provided for handling of big data. A data management process can account for attributes of data by analyzing or interpreting the data, assigning intervals to the attributes based on the data, and effectuating policies, based on the attributes and intervals, that facilitate data management. In addition, the data management process can determine relations among data in a data collection and generate and store approximate results concerning the data based on the attributes, intervals, and the policies.

Abstract: Techniques for efficiently performing queries are provided. A search component can receive a request for information based on data, and a management component can determine a degree of accuracy requested for the information. In turn, the search component can render the information based on the degree of accuracy requested. In an aspect, the search generates a query configured to determine the first information, and the management component instructs the search component to perform the query to a level of completion less than full completion when the degree of accuracy requested is below a predetermined threshold to cause the search component to render an estimation of the first information. In another aspect, a tracking component can track information associated with multiple query requests and an analysis determine and employ a related aspect of the tracked information to a new query request to determine an answer for a the new query request.

Abstract: Techniques for defocusing queries over big datasets and dynamic datasets are provided to broaden search results and incorporate all potentially relevant data and avoid overly narrowing queries. An analytic component can receive queries directed at one region of a dataset and analyze the queries to generate inferences about the queries. The queries can then be defocused by a defocusing component and incorporate a larger dataset than originally searched to broaden the queries. The larger dataset can incorporate all, or a part of the original dataset and can also be disparate from the original dataset. Clusters of queries can also be merged and unified to deal with ‘local minima’ issues and broaden the understanding of the dataset. In other embodiments, dynamic data can be monitored and changes tracked, to ensure that all portions of the dataset are being searched by the queries.

Abstract: The subject disclosure relates to using structured query language constructs in non-structured query language domains. For example, through mathematical and logical transformation of concepts from a key, value pair domain associated with structured query language data structures to graphical-related data structures, the value originating in the structured query language domain can be modified for use in non-structured query language domains. This can open up options in analytics and can solve some of the problems associated with liner algebra.

Abstract: A framework may be provided for facilitating adoption of mathematical and logical solvers. A model, representing a problem, may be defined. The model may be defined: via a series of statements in a type safe common-language-specification compliant modeling language; via an intermediate format; or via a series of calls to a standardized application program interface. The framework may translate the defined model to an intermediate format, which many solvers may understand. The framework may provide a number of solver-related services including, but not limited to, services for creating a model, analyzing a model, selecting a well-suited solver based on analysis of a model, decomposing a model into multiple sub-models and providing the multiple sub-models to multiple solvers as multiple parallel threads, data binding, simulation, and post optimality analysis.

Abstract: A model, which defines a mathematical problem, and multiple directives may be received. Each of the multiple directives may be mapped to a respective linear solver instance. The linear solver instances may be launched to execute in parallel. Each of the linear solver instances may use either a primal or a dual algorithm and may further use double arithmetic, exact arithmetic, or hybrid arithmetic, as specified by corresponding ones of the multiple directives. A linear solver instance that uses hybrid arithmetic may start by using double arithmetic and may use exact arithmetic after experiencing a numerical difficulty. After the numerical difficulty is resolved, the linear solver instance that uses hybrid arithmetic may restart and continue to solve the mathematical problem using double arithmetic. After one of the linear solver instances finds an optimal solution, others of the linear solver instances may be stopped and a report may be provided.

Abstract: A user's experience with internet content may be given semantic meaning based upon extracting features of the content and creating kind classifications from the features. Kind classifications may be used to enrich a user's experience with internet content by providing meaningful navigation and discovery of information. As provided herein, a data stream (e.g., HTML, audio, video, unstructured data, etc.) is received, and features (e.g., text, phrases, titles, paragraphs, image data, etc.) may be extracted from the data stream. Kind classifications may be created based upon the extracted features. For example, a shirt image kind classification may be created based upon a button image feature, a collar image feature, and a sleeve image feature. The user's experience may be enriched by a presentation of actions allowing the user to view similar shirts, purchase the shirt, and/or discover other information relating to the shirt, for example.

Abstract: A model, which defines a mathematical problem, and multiple directives may be received. Each of the multiple directives may be mapped to a respective linear solver instance. The linear solver instances may be launched to execute in parallel. Each of the linear solver instances may use either a primal or a dual algorithm and may further use double arithmetic, exact arithmetic, or hybrid arithmetic, as specified by corresponding ones of the multiple directives. A linear solver instance that uses hybrid arithmetic may start by using double arithmetic and may use exact arithmetic after experiencing a numerical difficulty. After the numerical difficulty is resolved, the linear solver instance that uses hybrid arithmetic may restart and continue to solve the mathematical problem using double arithmetic. After one of the linear solver instances finds an optimal solution, others of the linear solver instances may be stopped and a report may be provided.

Abstract: A framework may be provided for facilitating adoption of mathematical and logical solvers. A model, representing a problem, may be defined. The model may be defined: via a series of statements in a type safe common-language-specification compliant modeling language; via an intermediate format; or via a series of calls to a standardized application program interface. The framework may translate the defined model to an intermediate format, which many solvers may understand. The framework may provide a number of solver-related services including, but not limited to, services for creating a model, analyzing a model, selecting a well-suited solver based on analysis of a model, decomposing a model into multiple sub-models and providing the multiple sub-models to multiple solvers as multiple parallel threads, data binding, simulation, and post optimality analysis.