Abstract: Indexing and matching records in a data management system by defining entity indexing attributes associated with system records, receiving an incoming data entity, selecting a set of entity candidates according to the entity indexing attributes, matching the incoming entity to an entity candidate, generating an analysis of the entity candidate selection according to entity attribute effectiveness, and revising the entity indexing attributes according to the analysis.

Abstract: Securely matching encrypted entities by receiving data, segmenting the data into a plurality of categories, selecting encryption key(s) according to a data category of the plurality of data categories, encrypting the data of the data category using the encryption key(s), and comparing the encrypted data to previously encrypted data of the data category.

Abstract: Candidate data record prioritization for match processing includes identifying candidate records for use in match processing to determine records that match to an incoming record. The candidates are grouped into buckets according to bucket roles, with each bucket correlating to a bucket role, and with each bucket role being defined by a unique record attribute set. The method obtains an effectiveness score for each of the bucket roles. The scores are measures of effectiveness of the bucket roles in identifying candidates that match to incoming data records. The method establishes an order of priority in which to process the candidates by prioritizing the buckets into an order based on the effectiveness scores for the bucket roles. The process then commences match processing to process the candidates in the established order of priority where the match processing processes candidates of a higher priority bucket before processing candidates of lower priority buckets.

Abstract: Embodiments include a computer-implemented method including identifying, by a primary computer device, a plurality of records, each record having one or more attributes; standardizing, by the primary computer device, each of the plurality of records; assigning, by the primary computer device, an index to one or more of the one or more attributes; providing, by the primary computer device, instructions for clustering the standardized plurality of records in parallel into one or more clusters, each cluster including records having the same index, the one or more clusters being in a group; receiving, by the primary computer device, one or more groups, each group including one or more clusters sharing a same index; and linking one or more of the plurality of records in a cluster with another one or more of the plurality of records in another cluster within a same group.

Abstract: The present disclosure relates to a method for a secure storage, matching and linking of data records. The method comprises: receiving a current data record having one or more attributes, each attribute having an attribute value. For each attribute of at least part of the attributes a predefined set of variations of the attribute value of the attribute may be generated. The received attribute values may be encrypted resulting in an encrypted record and the generated sets of variations may be encrypted. The encrypted record may be stores in a storage system in association with the respective encrypted sets of variations.

Abstract: A system and method for intelligent visualization of unstructured data in a column-oriented data table includes receiving unstructured data from a plurality of sources for recording into the column-oriented data table having a database schema using a plurality of keys to visualize one or more attributes in the column-oriented data table, determining that a semantically related key is used to visualize the one or more attributes contained in the unstructured data received from the plurality of sources, wherein the semantically related key is different from the plurality of keys and is not synchronized with the database schema of the column-oriented data table, formatting the unstructured data to synchronize the unstructured data with the database schema of the column-oriented data table, and outputting the synchronized unstructured data into the column-oriented data table so that the unstructured data is visualized according to the database schema of the column-oriented data table.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: Embodiments of the present invention provides methods, computer program products, and systems for normalizing confidence score thresholds across systems in a federated environment. Embodiments of the present invention can be used to calculate normalization factors for participating hubs in a federated environment to normalize confidence score thresholds applied by those hubs and improve search results obtained therefrom.

Abstract: Systems, methods, and computer-readable media are disclosed for associating and reconciling disparate key-value pairs corresponding to a target entity across multiple organizational entities using a distributed match. A shared output mapping may be generated that associates and reconciles common and/or conceptually aligned key-value pairs across the multiple organizational entities. The shared output mapping allows any given organizational entity to leverage information known to other organizational entities about a target entity. In this manner, the organizational entities participate in an information sharing ecosystem that enables each organizational entity to provide a user with a more optimally customized user experience based on the greater breadth of information available through the shared output mapping.

Abstract: Embodiments of the present invention provides methods, computer program products, and systems for normalizing confidence score thresholds across systems in a federated environment. Embodiments of the present invention can be used to calculate normalization factors for participating hubs in a federated environment to normalize confidence score thresholds applied by those hubs and improve search results obtained therefrom.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: In multilevel clustering for a face recognition process, the first stage clustering is performed on each computing node, using the first x vector coefficients. From the resulting k clusters created in the first stage, a limited number of clusters are selected on which the second stage clustering is performed, using the next y vector coefficients. The search for a matching image is then limited to these selected clusters. Computational costs are reduced at the first stage clustering by using just the first x vector coefficients. Computational costs for the second stage clustering are also reduced by performing the second stage only with the limited number of clusters on a limited number of computing nodes. In this manner, the overall computational costs in the face recognition process is significantly reduced while maintaining a desired level of accuracy.

Abstract: In multilevel clustering for a face recognition process, the first stage clustering is performed on each computing node, using the first x vector coefficients. From the resulting k clusters created in the first stage, a limited number of clusters are selected on which the second stage clustering is performed, using the next y vector coefficients. The search for a matching image is then limited to these selected clusters. Computational costs are reduced at the first stage clustering by using just the first x vector coefficients. Computational costs for the second stage clustering are also reduced by performing the second stage only with the limited number of clusters on a limited number of computing nodes. In this manner, the overall computational costs in the face recognition process is significantly reduced while maintaining a desired level of accuracy.

Abstract: In multilevel clustering for a face recognition process, the first stage clustering is performed on each computing node, using the first x vector coefficients. From the resulting k clusters created in the first stage, a limited number of clusters are selected on which the second stage clustering is performed, using the next y vector coefficients. The search for a matching image is then limited to these selected clusters. Computational costs are reduced at the first stage clustering by using just the first x vector coefficients. Computational costs for the second stage clustering are also reduced by performing the second stage only with the limited number of clusters on a limited number of computing nodes. In this manner, the overall computational costs in the face recognition process is significantly reduced while maintaining a desired level of accuracy.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.