Dixit, P.M. & van der Aalst, W.M.P. (2018). Fast conformance analysis based on activity log abstraction. 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, Proceedings (pp. 135-144). Piscataway.
Process mining techniques focus on bridging the gap between activity logs and business process management. Process discovery is a sub-field of process mining which uses activity logs in order to discover process models. Some process discovery techniques, such as interactive process discovery and genetic algorithms, rely on the so-called conformance analysis. In such techniques, process models are discovered in an incremental way, and the quality of the process models is quantified by the results of conformance analysis. State-of-the-art conformance analysis techniques are typically optimized and devised for one-time use. However, in process discovery settings which are incremental in nature, it is imperative to have fast conformance analysis. Moreover, the activity logs used for conformance analysis at each stage remain the same. In this paper, we propose an approach that exploits this fact in order to expedite conformance analysis by approximating the conformance results. We use an abstracted version of an activity log, which can be used to compare withthe changing (or new) process models in an incremental processdiscovery setting. Our results show that the proposed technique isable to outperform traditional conformance techniques in terms of performance by approximating conformance scores.