Abstract: Techniques for data evaluation are provided. A plurality of data records is received, and a first comparison vector is generated by comparing a first and a second data record of the plurality of data records, where the first comparison vector indicates differences between the first and second data records. A machine learning model is trained based at least in part on the first comparison vector. The plurality of data records is evaluated using the machine learning model, and at least two of the plurality of data records are linked based on the evaluation.

Abstract: A method for relating different types of records. The method may include providing comparison functions, wherein each comparison function corresponds to a semantical class, and wherein a computational cost is associated with each comparison function. The method may include determining one or more attribute pairs between the different types of records. The method may include sorting the comparison functions according to a determined accuracy. The method may include selecting a set of comparison functions associated with semantical classes according to a predefined rule. The method may include determining a total computational cost based on the computational cost of the selected set of comparison functions. The method may include determining whether two or more records are related using the selected set of comparison functions. The method may include relating the two or more records. The method may include determining a rate of false negative records.

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: 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: 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: Matching records in an entity resolution system by defining entity attribute feature vectors, determining an entity attribute matching score according to a distance between two entity attribute feature vectors, assigning a statistical weight to an entity attribute matching score, adjusting the entity attribute matching score according to the statistical weight and an entity attribute frequency of occurrence, and determining an aggregate entity attribute matching score.

Abstract: A method, apparatus, computer system, and computer program product for managing information. A set of bucket hashes and comparison information for a data record are identified by a computer system. The set of bucket hashes is generated from the comparison information, wherein the set of bucket hashes and the comparison information form a metadata record. A number of candidate metadata records in a metadata database is identified by the computer system using the set of bucket hashes, wherein the number of candidate metadata records comprises a set of candidate bucket hashes and candidate comparison information. An entity membership is identified by the computer system for the data record from a comparison of the comparison information in the metadata record with the candidate comparison information in the number of candidate metadata records.

Abstract: Matching records in an entity resolution system by defining entity attribute feature vectors, determining an entity attribute matching score according to a distance between two entity attribute feature vectors, assigning a statistical weight to an entity attribute matching score, adjusting the entity attribute matching score according to the statistical weight and an entity attribute frequency of occurrence, and determining an aggregate entity attribute matching score.

Abstract: Techniques for data evaluation are provided. A plurality of data records is received, and a first comparison vector is generated by comparing a first and a second data record of the plurality of data records, where the first comparison vector indicates differences between the first and second data records. A machine learning model is trained based at least in part on the first comparison vector. The plurality of data records is evaluated using the machine learning model, and at least two of the plurality of data records are linked based on the evaluation.

Abstract: A method for relating different types of records. The method may include providing comparison functions, wherein each comparison function corresponds to a semantical class, and wherein a computational cost is associated with each comparison function. The method may include determining one or more attribute pairs between the different types of records. The method may include sorting the comparison functions according to a determined accuracy. The method may include selecting a set of comparison functions associated with semantical classes according to a predefined rule. The method may include determining a total computational cost based on the computational cost of the selected set of comparison functions. The method may include determining whether two or more records are related using the selected set of comparison functions. The method may include relating the two or more records. The method may include determining a rate of false negative records.