Cognitive data outlier pre-check based on data lineage

A computer program product comprising one or more computer readable storage media and collectively stored program instructions on the one or more computer readable storage media, the stored program instructions comprising:

• program instructions programmed to obtain pre-check data associated with data nodes, wherein the pre-check data includes data associated with a production environment;
• program instructions programmed to generate a machine learning data graph model based on abstracting data and data flows of the production environment, wherein each node of a plurality of nodes of the data graph model represents different data and an edge of the data graph model represents a relationship between nodes of the data graph;
• program instructions programmed to determine a value for each edge of the data graph model based on an attenuation factor;
• program instructions programmed to determine one or more specified nodes from the plurality of nodes of the data graph model;
• program instructions programmed to determine initial hyperparameters for the data graph model based at least on historical data associated with the production environment represented by the data graph model, wherein the hyperparameters include the attenuation factor;
• program instructions programmed to calculate outliers for each specified data node by an outlier detection algorithm, wherein the outliers are calculated based on a unit of the pre-check data associated with each specified data node;
• program instructions programmed to backtrack the calculated outliers for each specified data node through an associated generating data link;
• program instructions programmed to generate preliminary node data based on an abstracted structure represented by the data graph;
• program instructions programmed to generate preliminary high-probability anomaly nodes as a set of largest outliers based on calculating outliers for the one or more specified nodes and backtracking the calculated preliminary outliers along input edges;
• program instructions to determine one or more of the preliminary high-probability anomaly nodes are true anomaly nodes;
• program instructions to generate an evaluation of the abstracted structure represented by the data graph model;
• program instructions programmed to select one or more data nodes and one or more data links associated with the set of largest outliers;
• and
• program instructions programmed to generate potential anomaly indications based on the one or more data nodes selected and the one or more data links selected.

A computer system comprising:

• a processor set;
• and
• a computer readable storage medium;
• wherein:
• the processor set is structured, located, connected and programmed to run program instructions stored on the computer readable storage medium;
• and
• the stored program instructions include:
• program instructions programmed to obtain pre-check data associated with data nodes, wherein the pre-check data includes data associated with a defined environment;
• program instructions programmed to generate a machine learning data graph model based on abstracting data and data flows of the production environment, wherein each node of a plurality of nodes of the data graph model represents different data and an edge of the data graph model represents a relationship between nodes of the data graph;
• program instructions programmed to determine a value for each edge of the data graph model based on an attenuation factor;
• program instructions programmed to determine one or more specified nodes from the plurality of nodes of the data graph model;
• program instructions programmed to determine initial hyperparameters for the data graph model based at least on historical data associated with the production environment represented by the data graph model, wherein the hyperparameters include the attenuation factor;
• program instructions programmed to calculate outliers for each specified data node by an outlier detection algorithm, wherein the outliers are calculated based on a unit of the pre-check data associated with each specified data node;
• program instructions programmed to backtrack the calculated outliers for each specified data node through an associated generating data link;
• program instructions programmed to generate preliminary node data based on an abstracted structure represented by the data graph;
• program instructions programmed to generate preliminary high-probability anomaly nodes as a set of largest outliers based on calculating outliers for the one or more specified nodes and backtracking the calculated preliminary outliers along input edges;
• program instructions to determine one or more of the preliminary high-probability anomaly nodes are true anomaly nodes;
• program instructions to generate an evaluation of the abstracted structure represented by the data graph model;
• program instructions programmed to select one or more data noes and one or more data links associated with the set of largest outliers;
• and
• program instructions programmed to generate potential anomaly indications based on the one or more data nodes selected and the one or more data links selected.