Business process models for visually navigating process execution data

Abstract

To analyze large amounts of data, visual analysis tools offer filter mechanisms for drilling down into multi-dimensional information spaces, or slicing and dicing them according to given criteria. This paper introduces an analysis approach for navigating multi-dimensional process instance execution logs based on business process models. By visually selecting parts of a business process model, a set of available log entries is filtered to include only those entries that result from execution instances of the selected process branches. Using this approach allows to exploratively navigate through process execution logs and analyze them according to the causal-temporal relationships encoded in the underlying business process model. The business process models used by the approach can either be created using model editors, or be statistically derived using process mining techniques. We exemplify our approach with a prototypical implementation.