Efficient analysis of process logs

The issue of devising efficient and effective solutions for supporting the analysis of process logs has recently received great attention from the research community, as effectively accomplishing any business process management task requires understanding the behavior of the processes. In this paper, we propose a new framework supporting the analysis of process logs, exhibiting two main features: a flexible data model (enabling an exhaustive representation of the facets of the business processes that are typically of interest for the analysis) and a graphical query language, providing a user-friendly tool for easily expressing both selection and aggregate queries over the business processes and the activities they are composed of. The framework has been implemented, and different physical organizations of the data have been tried: in particular, both the "traditional" technology of two relational DBMSs and the "innovative" Big-Data-oriented technology of the NoSQL DBMS Neo4J have been considered. The comparative analysis between these implementations is a contribution of independent interest: besides assessing the capability of the framework to be a support for evaluating queries over process logs, it gives an insight on the differences between the relational and the graph-based models in terms of efficiency of evaluation of the typical queries posed during business process analysis.