Abstract: An embodiment of the present invention is directed to implementing a data usage analytics engine for database systems. An embodiment of the present invention is directed to implementing a Data Usage Analysis engine that receives queries (e.g., SQL queries), tables (e.g., Internal Catalog tables) and/or other data formats as input. An embodiment of the present invention may then parse the queries and identify various data usage patterns. This may include details concerning what tables are used, how much data is queried at what intervals, frequency of querying along what attributes are used in the queries and/or other usage details in various levels of granularity.

Abstract: Various methods, apparatuses/systems, and media for identifying production incidents and implementing automated preventive and corrective measures are disclosed. A processor automatically triggers, in response to a generated incident of a job/process/host failure, a self-healing service. The processor identifies an application to which the event generated belongs to by accessing a database that stores the application and host details; fetches functional identification (ID) of the application from the database, identifies the type of job failure or service degradation; automatically executes, by utilizing predefined micro services, the steps required for mitigation; records, in response to executing, outcome of the mitigation in the database along with output at each stage of execution; and evaluates the outcome of the mitigation by executing health checks using micro services to determine whether the failed job or process or host is healthy; and closes the incident based on healthy determination.

Abstract: An embodiment of the present invention is directed to classifying attributes into respective PI/PG categories based on metadata. An embodiment of the present invention may classify each attribute into PII/Non-PII and then into various Protection group codes that define access, roles permissions, privileges and/or other action. An embodiment of the present invention may leverage various statistical techniques, natural language processing (NLP) methods and different combinations of algorithms customized to improve prediction accuracies of a classifier model.

Abstract: A method and a system for dynamically tracking data lineage is provided. The method includes: assigning a first context key to a first data set; inputting the first data set into a first software module; receiving a second data set as an output of the first software module; and assigning a second context key to the second data set. The second context key includes information that identifies the inputted first data set and information that identifies the first software module. When the second data set is subsequently inputted into a second software module, a third context key that identifies the inputted second data set and the second software module is assigned to the resultant output data set.

Abstract: Various methods, apparatuses/systems, and media for identifying production incidents and implementing automated preventive and corrective measures are disclosed. A processor automatically triggers, in response to a generated incident of a job/process/host failure, a self-healing service. The processor identifies an application to which the event generated belongs to by accessing a database that stores the application and host details; fetches functional identification (ID) of the application from the database, identifies the type of job failure or service degradation; automatically executes, by utilizing predefined micro services, the steps required for mitigation; records, in response to executing, outcome of the mitigation in the database along with output at each stage of execution; and evaluates the outcome of the mitigation by executing health checks using micro services to determine whether the failed job or process or host is healthy; and closes the incident based on healthy determination.

Abstract: A method and a system for dynamically tracking data lineage is provided. The method includes: assigning a first context key to a first data set; inputting the first data set into a first software module; receiving a second data set as an output of the first software module; and assigning a second context key to the second data set. The second context key includes information that identifies the inputted first data set and information that identifies the first software module. When the second data set is subsequently inputted into a second software module, a third context key that identifies the inputted second data set and the second software module is assigned to the resultant output data set.

Abstract: Systems and methods for connecting applications based on exchanged information are disclosed. According to one embodiment, a method may include: (1) receiving, by a data mining computer program, application information for a plurality of applications in an organization; (2) receiving, by the data mining computer program, application details for each application comprising application programmable interface (API) used by each application; (3) receiving, by the data mining computer program, API information for each API in the application details; (4) populating, by the data mining computer program, an application data objects table for each application; (5) creating, by the data mining computer program, parent-child relationships for the plurality of applications based on the application data objects table; and (6) generating, by the data mining computer program, an application dependency graph for the parent-child relationships.

Abstract: An embodiment of the present invention is directed to classifying attributes into respective PI/PG categories based on metadata. An embodiment of the present invention may classify each attribute into PII/Non-PII and then into various Protection group codes that define access, roles permissions, privileges and/or other action. An embodiment of the present invention may leverage various statistical techniques, natural language processing (NLP) methods and different combinations of algorithms customized to improve prediction accuracies of a classifier model.

Abstract: An embodiment of the present invention is directed to implementing a data usage analytics engine for database systems. An embodiment of the present invention is directed to implementing a Data Usage Analysis engine that receives queries (e.g., SQL queries), tables (e.g., Internal Catalog tables) and/or other data formats as input. An embodiment of the present invention may then parse the queries and identify various data usage patterns. This may include details concerning what tables are used, how much data is queried at what intervals, frequency of querying along what attributes are used in the queries and/or other usage details in various levels of granularity.