Abstract: Computer system for performing biometric matching in a way that balances accuracy level required in the biometric matching against computing resources (for example, processor cycles) that will be needed to match authentication requesters with profiles of authorized users. In some embodiments, this is achieved by controlling the number of clusters and/or the number of clusters to be searched pursuant to an authentication request.

Abstract: Methods, computer program products and/or systems are provided that perform the following operations: obtaining payload attribute fields; determining potential matching fields from the payload attribute fields; determining a matching function for each of the potential matching fields; determining an attribute score for each of the potential matching fields based on the matching function; obtaining a score list for a reference data set; determining a correlation of the attribute score for each of the potential matching fields with the reference data set score list; selecting new matching fields from the potential matching fields based at least in part on the correlation; determining an optimal weight for each of the selected new matching fields; selecting attribute fields for matching from the selected new matching fields based on a threshold rate for false positives and false negatives; and providing the attribute fields for matching and the associated optimal weight for the attribute fields.

Abstract: A method for receiving an additional dataset including a plurality of additional data records; determining a record type using classifiers and an internal domain knowledge corpus; dividing the plurality of additional data records into a plurality of indexing groups; assigning the given additional data record to a match set based on completeness and similarity of natures of attributes of the given additional data record; and assigning the given additional data record to and a comparison group based on completeness and similarity of natures of attributes of the given additional data record.

Abstract: A method is disclosed for a database system that includes a set of data blocks comprising records having attributes. The set of data blocks are instances of at least one block type. The block type is defined by a subset of one or more attributes of the attributes. An instance of the block type comprises records having one distinct group of values of the subset of attributes. The method includes detecting that a subset of one or more data blocks of the block type, of the set of data blocks, reached a first maximum number of records. The method includes determining an additional attribute of the attributes to define a new block type by the combination of the additional attribute and the subset of attributes that define the block type. The method includes creating one or more data blocks which are instances of the new block type.

Abstract: Configuring a data management system by receiving user interaction data associated with search results associated with a first system configuration, identifying a usage pattern in the user interaction data using a first machine learning model, and altering the first system configuration according to the usage pattern.

Abstract: Computer system for performing biometric matching in a way that balances accuracy level required in the biometric matching against computing resources (for example, processor cycles) that will be needed to match authentication requesters with profiles of authorized users. In some embodiments, this is achieved by controlling the number of clusters and/or the number of clusters to be searched pursuant to an authentication request.

Abstract: An approach is provided for providing optimized identification of duplicate data in a networked computing environment. An aggregate feature vector is created that is specific to an attribute of the data (e.g., a field that holds specific informational content). The aggregate feature vector has a set of dimensions that each define a specific comparison function used to test for similarity between data entries in the attribute. Each dimension in the aggregate feature vector is assigned an effectiveness, and a cost is computed for each dimension. Based on these two, a subset of dimensions is selected to form an optimized feature vector. This optimized feature vector can then be used to analyze a dataset to find matching data.

Abstract: A method, a structure, and a computer system for mapping data fields. The exemplary embodiments may include, based on determining that a first data set and a second data set contain homogenous data, mapping at least one column of the first data set to at least one column of the second data set based on comparing at least one of relative column position and unique value sets. Based on determining that the first data set and the second data set contain heterogeneous data, the exemplary embodiments may include mapping the at least one column of the first data set to the at least one column of the second data set based on a difference between distribution signatures of unique value sets within each of the first data set and the second data set being less than a threshold.

Abstract: A method, a structure, and a computer system for event-based record matching. The exemplary embodiments may include extracting one or more events from a first record and a second record, as well as calculating an event-based score based on comparing the one or more events extracted from the first record with the one or more events extracted from the second record. The exemplary embodiments may further include matching the first record to the second record based on the event-based score exceeding a threshold.

Abstract: An approach is provided for providing optimized identification of duplicate data in a networked computing environment. An aggregate feature vector is created that is specific to an attribute of the data (e.g., a field that holds specific informational content). The aggregate feature vector has a set of dimensions that each define a specific comparison function used to test for similarity between data entries in the attribute. Each dimension in the aggregate feature vector is assigned an effectiveness, and a cost is computed for each dimension. Based on these two, a subset of dimensions is selected to form an optimized feature vector. This optimized feature vector can then be used to analyze a dataset to find matching data.

Abstract: A method for receiving an additional dataset including a plurality of additional data records; determining a record type using classifiers and an internal domain knowledge corpus; dividing the plurality of additional data records into a plurality of indexing groups; assigning the given additional data record to a match set based on completeness and similarity of natures of attributes of the given additional data record; and assigning the given additional data record to and a comparison group based on completeness and similarity of natures of attributes of the given additional data record.

Abstract: Configuring a data management system by receiving user interaction data associated with search results associated with a first system configuration, identifying a usage pattern in the user interaction data using a first machine learning model, and altering the first system configuration according to the usage pattern.

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: One embodiment provides a computer implemented method, including: obtaining an information document corresponding to an entity, wherein the information document includes redacted information spans; identifying an entity type for each of the redacted information spans, wherein the entity type identifies a relationship between a redacted information span and at least one other entity within the information document; replacing the redacted information spans with replacement entities corresponding to the entity type of a given redacted information span, wherein the replacing is performed in view of a frequency distribution of actual information and wherein the replacing includes maintaining relationships of the redacted information spans; and controlling bias within the replacement entities, wherein the controlling includes detecting bias within the replacement entities.

Abstract: A computer-implemented method increases the efficiency of matching records from two sources. The method includes identifying a first source and a second source wherein each of the sources include one or more records and each record includes one or more attributes. The method further includes determining, based on a corpus, the one or more attributes and generating, based on the attributes, a set of feature vectors which vectors represent the one or more attributes. The method includes comparing each record in the first source against each record in the second source. The method further includes generating, in response to the comparing, a link confidence. The method also includes linking, in response to the link confidence being above a linking threshold, the associated records. The method includes determining a first feature vector of the set of feature vectors used in the linking, and outputting a set of results.

Abstract: Methods, computer program products and/or systems are provided that perform the following operations: obtaining payload attribute fields; determining potential matching fields from the payload attribute fields; determining a matching function for each of the potential matching fields; determining an attribute score for each of the potential matching fields based on the matching function; obtaining a score list for a reference data set; determining a correlation of the attribute score for each of the potential matching fields with the reference data set score list; selecting new matching fields from the potential matching fields based at least in part on the correlation; determining an optimal weight for each of the selected new matching fields; selecting attribute fields for matching from the selected new matching fields based on a threshold rate for false positives and false negatives; and providing the attribute fields for matching and the associated optimal weight for the attribute fields.

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: A method is disclosed for a database system that includes a set of data blocks comprising records having attributes. The set of data blocks are instances of at least one block type. The block type is defined by a subset of one or more attributes of the attributes. An instance of the block type comprises records having one distinct group of values of the subset of attributes. The method includes detecting that a subset of one or more data blocks of the block type, of the set of data blocks, reached a first maximum number of records. The method includes determining an additional attribute of the attributes to define a new block type by the combination of the additional attribute and the subset of attributes that define the block type. The method includes creating one or more data blocks which are instances of the new block type.

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.