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.

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.

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.

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 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.

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.

A location data processing apparatus includes a data enrichment module arranged to receive, when in use, content generated in response to a content request. The data enrichment module is arranged to embed location importation data in the content prior to presentation thereof. In at least one embodiment, the location importation data is organized in accordance with a data structure definition.

A method for knowledge generation from raw medical records uses XML-based specifications. The method includes content extraction, data aggregation and data enrichment. The method operates on various sources of medical data including financial data, clinical documents and medical images.

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.

Described are methods, systems, and media for enriching data records while preserving data provenance to enable historical data enrichment process replay.

A system and a method for automating performance detection of one or more application programming interfaces (APIs) is provided. The present invention provides for retrieving one or more test cases and associated test data as per respective test case ID's and generate one or more test requests by applying a data enrichment technique. Further, the present invention provides for executing one or more generated test requests on an API under test, analyze a response received from the API under test, perform response validation, detect any defects in the API based on the received response, and generate a detailed report of the executed test request. Furthermore, the present invention provides a visual interface for selecting test cases, creating test cases, editing test cases, editing test requests, display execution of test requests and test reports.

A method for knowledge generation from raw medical records uses XML-based specifications. The method includes content extraction, data aggregation and data enrichment. The method operates on various sources of medical data including financial data, clinical documents and medical images.

A data enrichment processing apparatus (100) comprises a processing resource (154) arranged to access, when in use, location data (300) having temporal data associated therewith, and to group a part of the location data according to a predetermined criterion. The processing resource (154) is arranged to support an analysis module (268) capable of inferring temporal access information from the part of the location data grouped, the temporal access information being indicative of ability to access physically a point of interest associated with the part of the location data grouped.

A data enrichment environment using blockchain is disclosed. A client may interact with the data enrichment environment to generate an enrichment smart contract and deploy the enrichment smart contract to the blockchain. The enrichment smart contract may comprise data indicating the enrichment inquiry. One or more solvers may interact with the data enrichment environment to submit proposed enrichment solutions based on the enrichment inquiry. One or more observers may interact with the data enrichment environment to review and vote on the proposed enrichment solutions. The data enrichment environment may evaluate the proposed enrichment solutions based on contract conditions.

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.

A data enrichment system for enriching requests for advertising opportunities. The data enrichment system is configured to aggregate and index data to provide end user insights to marketers based upon information supplied by publishers in regards to advertising opportunities passed along in requests. The system is configured to take attributes passed along in a request associated with the advertising opportunity and provide enriched data based upon the attributes received in the request. The attributes can then identify corresponding enriched data that can be passed along to the marketers.

Data quality enrichment integration and evaluation system that enables the import of data into a database and the turnkey integration with data enrichment entities. The data imported into the database may be validated using validation rules. Data with particular data quality problems may be sent to a particular bucket for to avoid processing or to obtain processing by a particular data quality enrichment entity. A bucket of data may be sent to be enriched by one or more data enrichment entities. The enrichment results may be compared between entities to enable the selection of a data enrichment entity. The enrichment results may also be drilled down into to provide geographic and other plots that show the quality of original data and quality of data enrichment provided by each data enrichment entity. Evaluation of the enrichment results side by side allows for the selection of a data enrichment entity.

Data enrichment and augmentation is disclosed. Machine data comprising at least one of a log message and a metrics data point is received. The received machine data comprises an identifier of an instance of a virtual machine. Based at least in part on the identifier of the instance of the virtual machine, a query for tags associated with the instance of the virtual machine is performed. At least one key-value pair is generated based at least in part on tags received in response to the query performed based at least in part on the identifier of the instance of the virtual machine. The received machine data is augmented with the at least one key-value pair generated based at least in part on the tags received in response to the query based at least in part on the identifier of the instance of the virtual machine.

A system and method for de novo drug discovery using machine learning algorithms. In a preferred embodiment, de novo drug discovery is performed via data enrichment and interpolation / perturbation of molecule models within the latent space, wherein molecules with certain characteristics can be generated and tested in relation to one or more targeted receptors. Filtering methods may be used to determine active novel molecules by filtering out non-active molecules and contain activity predictors to better navigate the molecule-receptor domain. The system may comprise neural networks trained to reconstruct known ligand-receptors pairs and from the reconstruction model interpolate and perturb the model such that novel and unique molecules are discovered. A second preferred embodiment trains a variational autoencoder coupled with a bioactivity model to predict molecules exhibiting a range of desired properties.

The invention provides a location data processing apparatus which comprises a data enrichment module (171) arranged to receive, when in use, content generated in response to a content request. The data enrichment module (171) is arranged to embed location importation data in the content prior to presentation thereof. The location importation data is organized in accordance with a data structure definition.

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.

A method for auto discovery of sensitive data may include: (1) receiving, at data enrichment computer program in a metadata processing pipeline, raw metadata from a plurality of different data sources; (2) enriching, by the data enrichment computer program, the raw metadata; (3) converting, by the data enrichment computer program, the raw metadata and the enhanced raw metadata into a sentence structure; (4) predicting, by a category prediction computer program in the metadata processing pipeline, a predicted category for the sentence structure; (5) identifying, by a sensitive data mapping computer program, a sensitive data category that is mapped to the predicted category based on a policy mapping rule; (6) determining, by the sensitive data mapping computer program, a risk classification rating for the predicted category; and (7) tagging, by the sensitive data mapping computer program, the data source associated with the metadata based on the risk classification rating.

Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

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 1pnt 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 1pnt 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.

The invention relates to the technical field of maintenance of rail traffic, in particular to a method for predicting SOE of a rail traffic lithium battery through large data. The method comprises thesteps of a data preparation step, a data arrangement step, a data characterization step, a target determination step, a data calculation step, a training verification step and an algorithm evaluationstep; hidden noise data is found through special cleaning means, so that the effects of good cleaning effect, high accuracy and the like are realized; in addition, model training and evaluation are carried out, different algorithms are selected for matching verification and are issued by using different models of machine learning through data import, so that a structured product is formed, and the prediction accuracy of the models is constantly improved along with time accumulation and data enrichment.

In an embodiment, a process for data enrichment and matching includes obtaining a first dataset associated with a first user from a first data source, where the first dataset includes records from a structured data source, and obtaining a corresponding second dataset associated with a second user. The process includes enriching at least one of the first dataset and the second dataset. The process includes merging the first dataset and the second dataset including by matching a set of attributes based at least in part on matching corresponding attributes, wherein at least one of the first dataset and the second dataset has been enriched. The process includes outputting the merged data.

Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

A data enrichment processing apparatus (100) comprises a processing resource (154) arranged to access, when in use, location data (300) having temporal data associated therewith, and to group a part of the location data according to a predetermined criterion. The processing resource (154) is arranged to support an analysis module (268) capable of inferring temporal access information from the part of the location data grouped, the temporal access information being indicative of ability to access physically a point of interest associated with the part of the location data grouped.