Towards semantic comparison of multi-granularity process traces

A process trace describes the steps taken in a workflow to generate a particular result. Understanding a process trace is critical to be able to verify data, enable its re-use and to make appropriate decisions. Given many process traces, each with a large amount of very low level information, it is a challenge to make process traces meaningful to different users. It is more challenging to compare two complex process traces generated by heterogeneous systems and having different levels of granularity. In this paper, we present a novel notion of multi-granularity process trace that attempts to capture the conceptual abstraction of large process traces at different levels of granularity by leveraging ontology information. Based on this notion, graph matching based algorithms with semantic filtering are developed to efficiently and effectively compute the similarity between two process traces by considering both structural similarity and semantic similarity. Our experiment using both real world and synthetic datasets demonstrates that our techniques provide a practical approach for process trace similarity measurement.