Abstract: A method includes determining a first number of logs of a first log type that are generated by a first data store at a first time for a first time interval. A first baseline number and a first threshold value are determined for the first data store. The first baseline number is an expected number of logs of the first log type generated by the first data store at the first time for the first time interval. In response to determining that the first number of logs differs from the first baseline number by more than the first threshold value, the first data store and the first log type are identified as degraded. A report is generated. The report includes an identification that the first data store is degraded and an identification that the first log type is degraded.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for creating an effective baseline for controlling software and programs running on a user device or virtual machine environment of an enterprise network. Historical data may be used to train one or more iterative machine learning processes for intelligently identifying relevant program characteristics and aiding or automating a determination as to a software or program's need, usage, redundancy, or security. Determinations regarding software or program classifications may be used for further review or automated control of software or program execution within a network environment.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for creating an effective baseline for controlling software and programs running on a user device or virtual machine environment of an enterprise network. Historical data may be used to train one or more iterative machine learning processes for intelligently identifying relevant program characteristics and aiding or automating a determination as to a software or program's need, usage, redundancy, or security. Determinations regarding software or program classifications may be used for further review or automated control of software or program execution within a network environment.