Extracting Event Data from Databases to Unleash Process Mining

Abstract

Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards “better” processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models “raw data” need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create “flat” event logs that can be analyzed using traditional process-mining techniques.