CPC G06N 5/022 (2013.01) [G06F 16/2379 (2019.01); G06N 3/08 (2013.01); G06N 5/025 (2013.01); G06F 16/9024 (2019.01); G06N 20/00 (2019.01)] | 14 Claims |
1. A computer system for unified data governance, comprising:
one or more computer devices each having one or more processors and one or more tangible storage devices; and
a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising instructions for:
a machine learning framework in communication with a suite of enterprise application servers via one or more connector components, wherein the machine learning framework populates a context graph with to-be-governed data from the suite of enterprise application servers, trains a plurality of machine learning models based on user-defined parameters, persists properties of the plurality of machine learning models back to the context graph, and displays, via a graphical user interface, a generated recommendations panel based on contextual findings of the plurality of machine learning models, wherein the generated recommendations panel comprises recommended related database tables based on a context asset, natural language reasons as to why particular database tables were recommended, a number of queries a given recommended related database table was used in, and out of compliance recommendations, and wherein a natural language reason provides a shared context comprising a number of terms shared between a recommended related database table and the context asset and a listing of the terms, and wherein an out of compliance recommendation provides a warning when a table, of the recommended related database tables, has not been cataloged and is used within a query, and wherein the context graph comprises a schematic context which describes how data is structured, a semantic context which captures data meaning, a usage context which captures activity on data, a business context which captures practices of a business unit, and a social and organizational context which captures relationships between users comprising users following each other on a social network and organization membership, wherein the machine learning framework provides contextual governance intelligence via implementation of a declarative syntax based on human-optimized config object notation (HOCON) to construct machine learning pipelines.
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