System and method to identify data flows and data mappings across systems
AssigneeAT&T Global Network Services Deutschland GmbH
InventorJoern Amand Franz
Life of this patent
Estimating the term…
Abstract
Aspects of the subject disclosure may include, for example, a processing system performing a method of receiving a plurality of data change transactions, wherein each data change transaction of the plurality of data change transactions comprises data object values, identifying by the processing system, data change groups indicating correlated data flows between databases by comparing data object values of the plurality of data change transactions, and determining data mappings of data objects stored in databases by statistical analysis of the correlated data flows. Other embodiments are disclosed.
Claims20
A device, comprising:
- a processing system including a processor;
- and
- a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
- prioritizing known positive matches of data object values for data objects from past data mappings of the data objects discovered in a plurality of data change transactions for databases in a plurality of database systems, thereby identifying data change groups indicating correlated data flows between the databases, wherein the prioritizing comprises using a machine learning (ML) algorithm trained on data element and metadata element analysis of the correlated data flows;
- identifying data mappings of the data objects by a statistical analysis of the correlated data flows of the data change groups, wherein the correlated data flows and statistics are determined by a ML examination of the plurality of data change transactions;
- and
- providing results of the data mappings of the data objects stored in the databases.
A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
- prioritizing known positive matches of data object values for data objects from past data mappings of the data objects discovered in a plurality of data change transactions for databases in a plurality of database systems, thereby identifying data change groups indicating correlated data flows between the databases, wherein the prioritizing comprises using a machine learning (ML) algorithm trained on data element and metadata element analysis of the correlated data flows;
- identifying data mappings of the data objects by a statistical analysis of the correlated data flows of the data change groups, wherein the correlated data flows and statistics are determined by a ML examination of the plurality of data change transactions;
- and
- reporting results of the data mappings of the data objects stored in the databases.
A method, comprising:
- prioritizing, by a processing system including a processor, known positive matches of data object values for data objects from past data mappings of the data objects discovered in a plurality of data change transactions for databases in a plurality of database systems, thereby identifying data change groups indicating correlated data flows between the databases, wherein the prioritizing comprises using a machine learning (ML) algorithm trained on data element and metadata element analysis of the correlated data flows;
- identifying, by the processing system, data mappings of the data objects by a statistical analysis of the correlated data flows of the data change groups, wherein the correlated data flows and statistics are determined by a ML examination of the plurality of data change transactions;
- and
- outputting, by the processing system, results of the data mappings of the data objects stored in the databases.
Dependencies are inferred from the claim text.