Examining Case Management Demand Using Event Log Complexity Metrics

One of the main goals of process mining is to automatically discover meaningful process models from event logs. Since these logs are the essential source of information for discovery algorithms, their quality is of high importance. In recent years, many studies on the quality of resulting process models have been conducted. However, the analysis of event log quality prior to the generation of models has been neglected. For example, yet there are no metrics which can measure the degree of event log quality that is needed so that discovery algorithms can be applied. Especially in the context of case management (CM) where processes are less structured, complex event logs reduce the effectiveness of the process discovery. In order to avoid mining impractical "spaghetti processes", it would be convenient to measure the event log complexity prior to discovery steps. In this paper, we provide our research results concerning the design and applicability of such metrics. First of all, they shall help to indicate whether the event log quality is sufficient for traditional process discovery. In case of very poor quality, they indicate the demand for more agile techniques (e.g. adaptive CM or agenda-driven CM).