Abstract: In general, embodiments of the present invention provide systems and computer readable media for implementing a single data integration platform that supports multiple data access interfaces to a single corpus of stored dynamic data collected from multiple data sources. In embodiments, the data integration platform includes a record tables layer that stores a group of data records and supports a CRUD interface for accessing the data records; a resolution mapping layer that stores a set of entities generated by a many-to-one mapping of data records to entities using entity resolution; and an entities layer that stores resolved entities which may be accessed via either a search interface based on search criteria or a hybrid search interface that supports “get via record id” queries.

Abstract: Systems, apparatus, and methods for determining unique contacts from a collection or pool of merchant data are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to determine programmatic match results indicating whether different instances of merchant data match (e.g., describe the same contact). The circuitry may further determine probabilities of precision or recall errors with the programmatic match results. Programmatic match results having a high probability of error may be annotated by a user to generate user match results. The user match results may be used to generate a more reliable contacts database including unique contacts, as well as to train and/or update the match scoring algorithm. As such, the accuracy of machine-implemented binary classification is improved.

Abstract: Systems, apparatus, and methods for determining unique contacts from a collection or pool of merchant data are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to determine programmatic match results indicating whether different instances of merchant data match (e.g., describe the same contact). The circuitry may further determine probabilities of precision or recall errors with the programmatic match results. Programmatic match results having a high probability of error may be annotated by a user to generate user match results. The user match results may be used to generate a more reliable contacts database including unique contacts, as well as to train and/or update the match scoring algorithm. As such, the accuracy of machine-implemented binary classification is improved.

Abstract: In general, embodiments of the present invention provide systems and computer readable media for implementing a single data integration platform that supports multiple data access interfaces to a single corpus of stored dynamic data collected from multiple data sources. In embodiments, the data integration platform includes a record tables layer that stores a group of data records and supports a CRUD interface for accessing the data records; a resolution mapping layer that stores a set of entities generated by a many-to-one mapping of data records to entities using entity resolution; and an entities layer that stores resolved entities which may be accessed via either a search interface based on search criteria or a hybrid search interface that supports “get via record id” queries.

Abstract: Systems, apparatus, and methods for determining unique contacts from a collection or pool of merchant data are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to: access first merchant data associated with a first merchant; access second merchant data associated with a second merchant; determine a match score based the first merchant data and the second merchant data indicating a likelihood of the first merchant being the same as the second merchant; determine a match score threshold; determine whether the match score exceeds the match score threshold; and in response determining the match score fails to exceed the match score threshold, determine the first merchant as being different from the second merchant. Some embodiments may provide for techniques for machine learning with merchant data training sets to determine match scores.

Abstract: In general, embodiments of the present invention provide systems and computer readable media for implementing a single data integration platform that supports multiple data access interfaces to a single corpus of stored dynamic data collected from multiple data sources. In embodiments, the data integration platform includes a record tables layer that stores a group of data records and supports a CRUD interface for accessing the data records; a resolution mapping layer that stores a set of entities generated by a many-to-one mapping of data records to entities using entity resolution; and an entities layer that stores resolved entities which may be accessed via either a search interface based on search criteria or a hybrid search interface that supports “get via record id” queries.

Abstract: Systems, apparatus, and methods for determining unique contacts from a collection or pool of merchant data are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to determine programmatic match results indicating whether different instances of merchant data match (e.g., describe the same contact). The circuitry may further determine probabilities of precision or recall errors with the programmatic match results. Programmatic match results having a high probability of error may be annotated by a user to generate user match results. The user match results may be used to generate a more reliable contacts database including unique contacts, as well as to train and/or update the match scoring algorithm. As such, the accuracy of machine-implemented binary classification is improved.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for automatic cleaning of entity resolution (ER) data persistently stored in a data repository.

Abstract: Systems, apparatus, and methods for determining unique contacts from a collection or pool of merchant data are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to: access first merchant data associated with a first merchant; access second merchant data associated with a second merchant; determine a match score based the first merchant data and the second merchant data indicating a likelihood of the first merchant being the same as the second merchant; determine a match score threshold; determine whether the match score exceeds the match score threshold; and in response determining the match score fails to exceed the match score threshold, determine the first merchant as being different from the second merchant. Some embodiments may provide for techniques for machine learning with merchant data training sets to determine match scores.

Abstract: Systems, apparatus, and methods for determining unique contacts from a collection or pool of merchant data are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to determine programmatic match results indicating whether different instances of merchant data match (e.g., describe the same contact). The circuitry may further determine probabilities of precision or recall errors with the programmatic match results. Programmatic match results having a high probability of error may be annotated by a user to generate user match results. The user match results may be used to generate a more reliable contacts database including unique contacts, as well as to train and/or update the match scoring algorithm. As such, the accuracy of machine-implemented binary classification is improved.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for automatic cleaning of entity resolution (ER) data persistently stored in a data repository.

Abstract: In general, embodiments of the present invention provide systems and computer readable media for implementing a single data integration platform that supports multiple data access interfaces to a single corpus of stored dynamic data collected from multiple data sources. In embodiments, the data integration platform includes a record tables layer that stores a group of data records and supports a CRUD interface for accessing the data records; a resolution mapping layer that stores a set of entities generated by a many-to-one mapping of data records to entities using entity resolution; and an entities layer that stores resolved entities which may be accessed via either a search interface based on search criteria or a hybrid search interface that supports “get via record id” queries.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media configured to receive configuration data describing a desired data set distribution, and, in response to receiving new data instances, use the configuration data and the new data instances to dynamically optimize the distribution of data already stored in a data reservoir that has been discretized into bins representing the desired data distribution.

Abstract: In general, embodiments of the present invention provide systems and computer readable media for implementing a single data integration platform that supports multiple data access interfaces to a single corpus of stored dynamic data collected from multiple data sources. In embodiments, the data integration platform includes a record tables layer that stores a group of data records and supports a CRUD interface for accessing the data records; a resolution mapping layer that stores a set of entities generated by a many-to-one mapping of data records to entities using entity resolution; and an entities layer that stores resolved entities which may be accessed via either a search interface based on search criteria or a hybrid search interface that supports “get via record id” queries.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media configured to receive configuration data describing a desired data set distribution, and, in response to receiving new data instances, use the configuration data and the new data instances to dynamically optimize the distribution of data already stored in a data reservoir that has been discretized into bins representing the desired data distribution.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.