Patent application number | Description | Published |
20110093467 | SELF-INDEXING DATA STRUCTURE - A machine based tool and associated logic and methodology are used in converting data from an input form to a target form using context dependent conversion rules, and in efficiency generating an index that may be utilized to access the converted data in a database. Once the data has been converted, an index data structure for each data object may be automatically generated that encodes one or more characteristics or attributes of the converted data so that an entity may access the data using the index structure. As an example, the one or more characteristics may include categories, subcategories, or other attributes of the data. | 04-21-2011 |
20110302554 | APPLICATION GENERATOR FOR DATA TRANSFORMATION APPLICATIONS - A utility is provided for generating applications for a variety of data conversion or handling application environments. A user can use a graphical user interface to purpose application adaptable modules to define a desired application. In one implementation, the user interface ( | 12-08-2011 |
20140059031 | Subject Matter Context Search Engine - A search system associates contextual metadata with search terms and/or stored terms to facilitate identification of relevant information. In one implementation, a search term is identified ( | 02-27-2014 |
20150032712 | Subject Matter Context Search Engine - A search system associates contextual metadata with search terms and/or stored terms to facilitate identification of relevant information. In one implementation, a search term is identified ( | 01-29-2015 |
20150193210 | APPLICATION GENERATOR FOR DATA TRANSFORMATION APPLICATIONS - A utility is provided for generating applications for a variety of data conversion or handling application environments. A user can use a graphical user interface to purpose application adaptable modules to define a desired application. In one implementation, the user interface ( | 07-09-2015 |
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 |