Process discovery and optimization using time-series databases, graph-analytics, and machine learning

Abstract

Implementations described herein relate to methods, systems, and computer-readable media to generate an alert or recommendation based on an activity log. In some implementations, a computer-implemented method includes receiving, at a processor, an activity log associated with one or more enterprise sub-processes for a first time period, determining, at the processor, change context data based on a comparison of the entity data and the associated interaction data with corresponding entity data and associated interaction data of a second time period, analyzing the change context data to determine atomic event data, providing the atomic event data associated with the enterprise process as input to a trained machine learning model, determining, using the trained machine learned model and the atomic event data, a predicted future state of the enterprise process, and generating and transmitting an alert or recommendation to one or more data sinks associated with the enterprise process.