43 results about "Data enrichment" patented technology

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.

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.

The present invention discloses a method, a system and a computer program product for spend data classification using selected taxonomies. The invention provides refresh classification tool and implementation classification tools for spend data classification. The invention further provides a web mining tool for determining unknown terms in spend data to obtain an enriched spend data for classification.

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.

A data base system is disclosed for storing meta-data about labeled predominantly non-text items in a collection of labeled predominantly non-text items (lpnt items). The system comprises a storage facility (10), a keyword generation facility (20) and a meta-data enrichment facility (30). The storage facility (10) stores respective meta-data for lpnt items (i) within said collection, said meta-data comprising at least one lpnt item classification (gi) and a lpnt item description (Ti), said classification defining classes. The keyword generation facility (20), which generates keyword data for respective classes, comprises a retrieval module for retrieving terms from lpnt item descriptions of lpnt items associated with said classes. The keyword generation facility (20) further comprises a term relevance determining module for assigning a relevance of said terms for said classes, for selecting terms from said retrieved terms on the basis of their assigned relevance and providing these selected terms as candidate keywords. The system further comprises a metadata enrichment facility (30) for adding a subset (Ki) of the provided candidate keywords (K(c)) as keywords to the meta-data of lpnt items of a class.