Abstract: The present disclosure relates to a method for a weighting graph comprising nodes representing entities and edges representing relationships between entities in accordance with one or more domains. The method comprises: pre-processing the graph comprising assigning weights to the nodes and/or the edges of the graph in accordance with a specific domain of the domains, wherein the weight indicates a domain specific data quality problem of attribute values representing an edge of the edges and/or an entity involved in that edge. The weighted graph may be provided for enabling a processing of the graph in accordance with the specific domain.

Abstract: A computer-implemented method includes determining, by a computer device, composite view rules for combining first data from a first data record and second data from a second data record to create a composite view of an entity in a master data management system; receiving, by the computer device, the first data; receiving, by the computer device, the second data; creating, by the computer device, the composite view from the first data and the second data based on the composite view rules; physically materializing the composite view on a storage device; preserving, by the computer device, the first data record; and preserving, by the computer device, the second data record.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

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 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: Embodiments of the present invention describe creating a mesh network of autonomous vehicles. Embodiments describe, for a set of autonomous vehicles configured to perform joint tasks, creating a plurality of subset networks of autonomous vehicles based on geographical proximity, wherein the plurality of subset networks of autonomous vehicles overlap to form a mesh network of autonomous vehicles, and wherein the mesh network of autonomous vehicles and the subset networks of autonomous vehicles support a non-centralized task assignment feature to assign tasks to autonomous vehicles in the mesh network. Additionally, embodiments describe sending a status signal to a first autonomous vehicle in the mesh network via a second autonomous vehicle in the mesh network, wherein the first autonomous vehicle is in a same subset network of autonomous vehicles as the second autonomous vehicle.

Abstract: Cognitive pre-loading of referenced content in electronic mail (e-mail) messages includes determining, using computer hardware, metadata for an electronic message directed to a user, detecting, using the computer hardware, a universal resource locator (URL) within a body portion of the electronic message, determining, using the computer hardware, a content type for the URL, and performing, using the computer hardware, natural language processing on the electronic message to determine an action importance corresponding to the URL. At least a portion of content specified by the URL can be pre-fetched from a data processing system based on the metadata, the content type of the URL, and the action importance. The electronic message and at least a portion of the content can be provided to a client device of the user.

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: Methods, computer program products and/or systems are provided that perform the following operations: obtaining a first graph comprising first nodes representing first entities and first edges representing relationships between first entities, the first nodes being associated with first entity attributes descriptive of the first entities represented by the first nodes, the first edges being associated with first edge attributes descriptive of the relationships represented by the first edges; determining a first subgraph for a certain node of the first nodes of the first graph, the first subgraph including the certain node and at least one neighboring node of the certain node; and determining a data quality issue regarding the certain node based, at least in part, on applying one or more applicable rules of a set of data quality rules to first entity attribute values and first edge attribute values of the first subgraph.

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: A method processes information. Training pairs are generated by a computer system using matching fields in matching pairs of records for a data type, wherein matches are present between the matching fields in the matching pairs of records. Similarities between the training pairs are determined by the computer system using an importance map with importance values for the matching fields. Shapley values are determined by the computer system using the training pairs and the similarities between the training pairs. The importance map is adjusted by the computer system using the Shapley values.

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 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 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 invention relates to a method for a model-driven extraction of event data representing an event occurring on a blockchain network by a computational device with access to the blockchain network. The computational device is configured as an ETL-device for executing an ETL-code to modify a data content of an external data structure external of the blockchain network using the extracted event data. The method comprising detecting the event, determining an event schema, providing and executing a the ETL-code. The ETL-code comprises machine-executable instructions for extracting the event data representing the detected event, transforming the extracted event data using the event schema to comply with a data model defining a logical structure of the external data structure and loading the transformed data to the external data structure to modify the data content of the external data structure.

Abstract: A method, computer system, and computer program product for providing recommendations about processing datasets. A set of machine learning models are provided for use in respectively determining data processing action performable on a dataset based on a respective set of features of the dataset. A current dataset is received. A set of features of the current dataset are determined. One or more data processing actions are generated to be executed on the current dataset, which are determined by at least two machine learning models of the provided set, based on the determined set of features of the current dataset. One or more of the data processing actions are performed on the current dataset.

Abstract: User information categorization using consent-based class rules is described. Consent from a user is received regarding at least one functional area where user information is shareable is received. Based on the consent, at least one data class that is permitted to be shared is determined. A user information designation is associated with the at least one data class and class rules are applied to user information associated with the user information designation based on the association between the user information designation and the at least one data class.