Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can condense a hierarchy in a data governance system, wherein the hierarchy comprises a root node and at least one child node comprising related sub-trees by determining, for a parent node in the hierarchy of governance system, governance terms and respective assignment relationships from a plurality of information assets, determining usage of the governance term in at least one of a plurality of governance rules, and marking a governance term of the plurality of governance terms for elimination based on the determined assignment relationships and the determined usage of the governance term in the plurality of governance rules. Embodiments of the present invention can then delete the governance term from the hierarchy if the governance term is marked for elimination.

Abstract: A database system can comprise records, each record including a set of attributes. The database system can further comprise database views, each database view representing a subset of the set of attributes. Data purpose objects indicating a subset of attributes of the set of attributes and a processing purpose can be stored. Each processing purpose can be associated with one or more entities that authorized access to the subset of attributes of the processing purpose. A request for data for a specific processing purpose and a selected view of the database views can be received. A data purpose object that indicates the specific processing purpose can be retrieved. The subset of attributes represented by the selected view can be compared with the subset of the attributes indicated in the retrieved data purpose object. Values of the subset of attributes of the selected view can be provided.

Abstract: The present disclosure relates to a method comprising providing a set of one or more records, each record of the set of records having a set of one or more attributes. Values of the set of attributes of the set of records may be input to a trained data representation learning model for receiving, as an output of the trained data representation model, a set of feature vectors representing the set of records respectively. The set of feature vectors may be stored.

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for protecting sensitive information. The exemplary embodiments may include using an inverted text index for evaluating one or more statistical measures of an index token of the inverted text index, using the one or more statistical measures for selecting a set of candidate tokens, extracting metadata from the inverted text index, associating the set of candidate tokens with respective token metadata, tokenizing at least one document resulting in one or more document tokens, comparing the one or more document tokens with the set of candidate tokens, selecting a set of document tokens to be masked, selecting at least part of the set of document tokens that comprises sensitive information according to the associated token metadata, masking the at least part of the set of document tokens, and providing one or more masked documents.

Abstract: Duplicating identification in a graph having a second version. The graph includes nodes representing entities and edges representing relationships between the entities. The method includes: providing a previous first version of the graph. At least two target nodes of the graph may be identified. The first version and the second version of the graph may be compared for determining a comparison score indicative of a similarity between the two target nodes. The comparison score may be used for determining whether the two target nodes are duplicates with respect to each other.

Abstract: Duplicating identification in a graph having a second version. The graph includes nodes representing entities and edges representing relationships between the entities. The method includes: providing a previous first version of the graph. At least two target nodes of the graph may be identified. The first version and the second version of the graph may be compared for determining a comparison score indicative of a similarity between the two target nodes. The comparison score may be used for determining whether the two target nodes are duplicates with respect to each other.

Abstract: A computer implemented method comprising processing the unstructured objects of each record of records of a database for identifying a set of one or more values of attributes in the unstructured objects of the each record. The sets of unstructured attribute values of two records of the database may be compared for determining a similarity level between the two sets. It may be determined whether the two records are representing a same entity based on the comparison result.

Abstract: The present disclosure relates to a method for enabling data integration. The method comprises collecting matching results of matching of records by a matching component over a time window. The number of false tasks of user defined tasks and system defined tasks in the collected matching results may be determined. The matching criterion used by the matching component may be adjusted to minimize the number of user defined tasks while the fraction of false tasks stays within a certain limit. The matching criterion may be replaced by the adjusted matching criterion for further usage of the matching component.

Abstract: A database system can comprise records, each record including a set of attributes. The database system can further comprise database views, each database view representing a subset of the set of attributes. Data purpose objects indicating a subset of attributes of the set of attributes and a processing purpose can be stored. Each processing purpose can be associated with one or more entities that authorized access to the subset of attributes of the processing purpose. A request for data for a specific processing purpose and a selected view of the database views can be received. A data purpose object that indicates the specific processing purpose can be retrieved. The subset of attributes represented by the selected view can be compared with the subset of the attributes indicated in the retrieved data purpose object. Values of the subset of attributes of the selected view can be provided.

Abstract: In an approach, a processor receives a first record from a first data source, where the first record comprises attributes, a second record from a second data source, where the second record comprises said attributes, a first individual quality rating for the attributes of the first record, and a second individual quality rating for the attributes of the second record. A processor, in response to inputting the first record and the second record into a probabilistic matching engine, receives a matching score for each of the respective attributes. A processor calculates a weighted matching score for each of the respective attributes by weighting the matching score for each of the respective attributes with the first individual quality rating and the second individual quality rating. A processor assesses whether the first record and the second record represent the same entity based on the weighted matching score.

Abstract: A system including a processor and a memory storing program instructions and a machine learning module. The machine learning module is configured for outputting one or more suggested data element change requests in response to receiving an initial data element change request. Execution of the program instructions causes the processor to receive the initial data element change request, receive the one or more suggested data element change requests in response to inputting the initial data element change request into the machine learning module, receive one or more subsequent data element change requests, detect if the one or more subsequent data element change requests contain the one or more suggested data element change requests, and provide an alert signal if the one or more suggested data element change requests are not detected within the one or more subsequent data element change requests.

Abstract: The present disclosure relates to a computer receiving a current training dataset. A first fraction of the training dataset comprises synthetic training data and a remaining second fraction of the training dataset comprising real-life training data. The real-life training data is user defined data and the synthetic training data is system defined data. A machine learning based engine is trained and may repeatedly be performed by using the current training dataset. In each iteration or a subset of the iterations, the training dataset is updated by adding real-life training data, thereby increasing the second fraction in the updated training dataset and reducing the first fraction of the synthetic training data.

Abstract: Provided is a technique for matching different user representations of a person in a plurality of computer systems may be provided. The technique includes collecting information sets about user representations from a plurality of computer systems; normalizing the information sets to a unified format; grouping the information sets in the unified format into indexing buckets based on a user name using a non-phonetic algorithm; determining a similarity score for each pair of information sets in each of the indexing buckets; classifying each information set pair into a set of classes based on the similarity scores, wherein the set of classes comprise at least matches and non-matches; and using a data structure for merging information of information set pairs classified as matches.

Abstract: Embodiments of the present invention provide for a method for duplicate determination in a graph. The graph comprises nodes representing entities and edges representing relationships between the entities. Embodiments of the present invention identify two target nodes in the graph. A neighborhood subgraph may be determined for each of the two nodes. The neighborhood subgraph includes the respective node. Embodiments of the present invention can then determine whether the two nodes are duplicates with respect to each other, based on a result of a comparison between the two subgraphs and based on a comparison of the two target nodes of the subgraphs.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can condense a hierarchy in a data governance system, wherein the hierarchy comprises a root node and at least one child node comprising related sub-trees by determining, for a parent node in the hierarchy of governance system, governance terms and respective assignment relationships from a plurality of information assets, determining usage of the governance term in at least one of a plurality of governance rules, and marking a governance term of the plurality of governance terms for elimination based on the determined assignment relationships and the determined usage of the governance term in the plurality of governance rules. Embodiments of the present invention can then delete the governance term from the hierarchy if the governance term is marked for elimination.

Abstract: Embodiments of the present invention provide for a method for duplicate determination in a graph. The graph comprises nodes representing entities and edges representing relationships between the entities. Embodiments of the present invention identify two target nodes in the graph. A neighborhood subgraph may be determined for each of the two nodes. The neighborhood subgraph includes the respective node. Embodiments of the present invention can then determine whether the two nodes are duplicates with respect to each other, based on a result of a comparison between the two subgraphs and based on a comparison of the two target nodes of the subgraphs.

Abstract: A computer-implemented method for configuring data deduplication is disclosed. The computer-implemented method includes receiving source data. The computer-implemented method further includes analyzing the source data, wherein analyzing the source data includes generating data profiling statistics from the source data and classifying attributes of the source data. The computer-implemented method further includes determining at least one data domain associated with the source data based, at least in part, on the data profiling statistics, the classified attributes, and ontology data. The computer-implemented method further includes determining, for the at least one data domain associated with the source data, a number of required matching algorithms for a data matching engine to execute data deduplication within the source data.

Abstract: Embodiments of the present invention determines duplicates in a graph. The graph comprises nodes representing entities and edges representing relationships between the entities. The method comprises: identifying at least two nodes in the graph. A neighborhood subgraph may be determined for each of the two nodes. The neighborhood subgraph includes the respective node. The method further comprises determining whether the two nodes are duplicates with respect to each other, based on a result of a comparison between the two subgraphs.

Abstract: A computer implemented method is used for sorting data elements of a given set. The method includes performing an evaluation of a first type of usage of each data element. The method includes determining a set of data element candidates dependent on the evaluation of the first type of usage. The method includes performing an evaluation of a second type of usage of each data element of the set of data element candidates. The method includes sorting the data elements of the set of data element candidates dependent on the evaluation of the second type of usage of each data element of the set of data element candidates. The method includes providing the sorted data elements of the set of data element candidates, and in response, receiving a request for a data processing based on the provided sorted data elements of the set of data element candidates.

Abstract: A computer implemented method comprising processing the unstructured objects of each record of records of a database for identifying a set of one or more values of attributes in the unstructured objects of the each record. The sets of unstructured attribute values of two records of the database may be compared for determining a similarity level between the two sets. It may be determined whether the two records are representing a same entity based on the comparison result.