Patent application number | Description | Published |
20130110853 | SQL CONSTRUCTS PORTED TO NON-SQL DOMAINS | 05-02-2013 |
20130117257 | QUERY RESULT ESTIMATION - 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. | 05-09-2013 |
20130117272 | SYSTEMS AND METHODS FOR HANDLING ATTRIBUTES AND INTERVALS OF BIG DATA - 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. | 05-09-2013 |
20130159228 | DYNAMIC USER EXPERIENCE ADAPTATION AND SERVICES PROVISIONING - The subject disclosure generally relates to dynamic user experience adaptation and services provisioning. A user experience component can provide a user experience (UX) to a user. The UX can include, but is not limited to, an operating system, an application (e.g., word processor, electronic mail, computer aided drafting, video game, etc.), a user interface, and so forth. A monitoring component can monitor feedback generated in association with interaction with the user experience by the user. An update component can analyze the feedback, and update a user model associated with the user based at least in part on the analysis, and an adaptation component can modify the user experience based at least in part the user model. | 06-20-2013 |
20160092090 | DYNAMIC VISUAL PROFILING AND VISUALIZATION OF HIGH VOLUME DATASETS AND REAL-TIME SMART SAMPLING AND STATISTICAL PROFILING OF EXTREMELY LARGE DATASETS - The present disclosure relates generally to a data enrichment service that automatically profiles data sets and provides visualizations of the profiles using a visual-interactive model within a client application (such as a web browser or mobile app). The visual profiling can be refined through end user interaction with the visualization objects and guide exploratory data visualization and discovery. Additionally, data sampling of heterogeneous data streams can be performed during ingestion to extract statistical attributes from multi-columnar data (e.g., standard deviation, median, mode, correlation coefficient, histogram, etc.). Data sampling can continue in real-time as data sources are updated. | 03-31-2016 |
20160092474 | DECLARATIVE LANGUAGE AND VISUALIZATION SYSTEM FOR RECOMMENDED DATA TRANSFORMATIONS AND REPAIRS - The present disclosure relates generally to a data enrichment service that extracts, repairs, and enriches datasets, resulting in more precise entity resolution and correlation for purposes of subsequent indexing and clustering. As the data enrichment service can include a visual recommendation engine and language for performing large-scale data preparation, repair, and enrichment of heterogeneous datasets. This enables the user to select and see how the recommended enrichments (e.g., transformations and repairs) will affect the user's data and make adjustments as needed. The data enrichment service can receive feedback from users through a user interface and can filter recommendations based on the user feedback. | 03-31-2016 |
20160092475 | AUTOMATED ENTITY CORRELATION AND CLASSIFICATION ACROSS HETEROGENEOUS DATASETS - The present disclosure describes techniques for entity classification and data enrichment of data sets. A data enrichment system is disclosed that can extract, repair, and enrich datasets, resulting in more precise entity resolution and classification for purposes of subsequent indexing and clustering. Disclosed techniques may include performing entity recognition to identify segments of interest that relate to an entity. Related data may be analyzed for classification, which can be used to transform the data for enrichment to its users. | 03-31-2016 |
20160092476 | DECLARATIVE EXTERNAL DATA SOURCE IMPORTATION, EXPORTATION, AND METADATA REFLECTION UTILIZING HTTP AND HDFS PROTOCOLS - Techniques are disclosure for a data enrichment system that enables declarative external data source importation and exportation. A user can specify via a user interface input for identifying different data sources from which to obtain input data. The data enrichment system is configured to import and export various types of sources storing resources such as URL-based resources and HDFS-based resources for high-speed bi-directional metadata and data interchange. Connection metadata (e.g., credentials, access paths, etc.) can be managed by the data enrichment system in a declarative format for managing and visualizing the connection metadata. | 03-31-2016 |
20160092557 | TECHNIQUES FOR SIMILARITY ANALYSIS AND DATA ENRICHMENT USING KNOWLEDGE SOURCES - The present disclosure relates to performing similarity metric analysis and data enrichment using knowledge sources. A data enrichment service can compare an input data set to reference data sets stored in a knowledge source to identify similarly related data. A similarity metric can be calculated corresponding to the semantic similarity of two or more datasets. The similarity metric can be used to identify datasets based on their metadata attributes and data values enabling easier indexing and high performance retrieval of data values. A input data set can labeled with a category based on the data set having the best match with the input data set. The similarity of an input data set with a data set provided by a knowledge source can be used to query a knowledge source to obtain additional information about the data set. The additional information can be used to provide recommendations to the user. | 03-31-2016 |