Abstract: A blockchain may be used to store transactions in an immutable ledger. The types of transactions may vary and the information from the transactions could be used to identify information about nodes in a particular network. One example operation may include one or more of identifying a number of nodes operating on a blockchain, determining a new blockchain transaction, and determining one or more of the nodes as having one or more characteristics based on the new blockchain transaction.

Abstract: A blockchain may be used to store transactions in an immutable ledger. The types of transactions may vary and the information from the transactions could be used to identify information about nodes in a particular network. One example operation may include one or more of identifying a number of nodes operating on a blockchain, determining a new blockchain transaction, and determining one or more of the nodes as having one or more characteristics based on the new blockchain transaction.

Abstract: A blockchain may be used to store transactions in an immutable ledger. The types of transactions may vary and the information from the transactions could be used to identify information about nodes in a particular network. One example operation may include one or more of identifying a number of nodes operating on a blockchain, determining a new blockchain transaction, and determining one or more of the nodes as having one or more characteristics based on the new blockchain transaction.

Abstract: A computer-implemented method includes detecting an update to a record in an entity table of a database. At least one of an age score, a lineage score, and a completeness score for the record is calculated, responsive to the update. A trust factor is calculated, by a computer processor, based on the at least one of the age score, the lineage score, and the completeness score for the record. The trust factor indicates a level of trustworthiness of the record. It is decided whether to use data in the record based on the trust factor.

Abstract: Determination of a degree of similarity among and between a set of text notation schema instances. One type of text notation schema instance is the JSON type. In some embodiments, the degree of similarity is expressed as a schema variance value which is determined by individually comparing the schema instances of the set of text notation schema instances to a representative majority schema. Also, determining a quality of a data source associated with the plurality of text notation schema instances based, at least in part, upon the similarity value.

Abstract: An approach for joining operations on document-oriented databases. The approach consists of receiving database identifiers, common attributes and results attributes for core and target databases being joined. Common attributes are searched for in the databases. The searches performed include string, expansive, character and nested. Common attribute join conflicts are identified and input is received to resolve attribute join conflicts. Resolved join conflicts are updated in a join substitution database for subsequent use and joined data results are output to virtual table(s).

Abstract: A blockchain may be used to store transactions in an immutable ledger. The types of transactions may vary and the information from the transactions could be used to identify information about nodes in a particular network. One example operation may include one or more of identifying a number of nodes operating on a blockchain, determining a new blockchain transaction, and determining one or more of the nodes as having one or more characteristics based on the new blockchain transaction.

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: An approach for joining operations on document-oriented databases. The approach consists of receiving database identifiers, common attributes and results attributes for core and target databases being joined. Common attributes are searched for in the databases. The searches performed include string, expansive, character and nested. Common attribute join conflicts are identified and input is received to resolve attribute join conflicts. Resolved join conflicts are updated in a join substitution database for subsequent use and joined data results are output to virtual table(s).

Abstract: An approach for joining operations on document-oriented databases. The approach consists of receiving database identifiers, common attributes and results attributes for core and target databases being joined. Common attributes are searched for in the databases. The searches performed include string, expansive, character and nested. Common attribute join conflicts are identified and input is received to resolve attribute join conflicts. Resolved join conflicts are updated in a join substitution database for subsequent use and joined data results are output to virtual table(s).

Abstract: A computer-implemented method includes detecting an update to a record in an entity table of a database. At least one of an age score, a lineage score, and a completeness score for the record is calculated, responsive to the update. A trust factor is calculated, by a computer processor, based on the at least one of the age score, the lineage score, and the completeness score for the record. The trust factor indicates a level of trustworthiness of the record. It is decided whether to use data in the record based on the trust factor.

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: An approach for joining operations on document-oriented databases. The approach consists of receiving database identifiers, common attributes and results attributes for core and target databases being joined. Common attributes are searched for in the databases. The searches performed include string, expansive, character and nested. Common attribute join conflicts are identified and input is received to resolve attribute join conflicts. Resolved join conflicts are updated in a join substitution database for subsequent use and joined data results are output to virtual table(s).

Abstract: An approach for joining operations on document-oriented databases. The approach consists of receiving database identifiers, common attributes and results attributes for core and target databases being joined. Common attributes are searched for in the databases. The searches performed include string, expansive, character and nested. Common attribute join conflicts are identified and input is received to resolve attribute join conflicts. Resolved join conflicts are updated in a join substitution database for subsequent use and joined data results are output to virtual table(s).

Abstract: An approach for joining operations on document-oriented databases. The approach consists of receiving database identifiers, common attributes and results attributes for core and target databases being joined. Common attributes are searched for in the databases. The searches performed include string, expansive, character and nested. Common attribute join conflicts are identified and input is received to resolve attribute join conflicts. Resolved join conflicts are updated in a join substitution database for subsequent use and joined data results are output to virtual table(s).

Abstract: Determination of a degree of similarity among and between a set of text notation schema instances. One type of text notation schema instance is the JSON type. In some embodiments, the degree of similarity is expressed as a schema variance value which is determined by individually comparing the schema instances of the set of text notation schema instances to a representative majority schema. Also, determining a quality of a data source associated with the plurality of text notation schema instances based, at least in part, upon the similarity value.

Abstract: A computer-implemented method includes receiving a column-oriented table comprising data for a column family, wherein the data for the column family comprises column names and corresponding column values, receiving a set of anonymous column names for the column family, receiving a set of synonymous column names for the column family, determining a weighting for each column name that is not an anonymous column name based on the count or frequency of occurrence of the column name and the synonymous column names within the column-oriented table, and processing the column-oriented table with a probabilistic matching engine using the weighting for each column name. A corresponding computer program product and computer system are also disclosed herein.

Abstract: Granular event management for service platforms. First bundle information relating to the content of a bundle is received at a first time, wherein the bundle relates to one or more resources in an application of a distributed computing environment. The bundle is monitored for a bundle event, wherein the bundle event is generated from a change in a lifecycle state of the bundle. Whether the bundle event has occurred is determined. Responsive to determining that the bundle event has occurred, second bundle information relating to the content of the bundle is received at a second time. Responsive to receiving the second bundle information, a granular event associated with the bundle event is identified, wherein the granular event relates to a difference between the first bundle information and the second bundle information.