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Conformance Checking Approximation Using Subset Selection and Edit Distance

 
: Fani Sani, M.; Zelst, S.J. van; Aalst, W.M.P. van der

:

Dustdar, S.:
Advanced Information Systems Engineering. 32nd International Conference, CAiSE 2020. Proceedings : Grenoble, France, June 8-12, 2020
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12127)
ISBN: 978-3-030-49434-6 (Print)
ISBN: 978-3-030-49435-3 (Online)
pp.234-251
International Conference on Advanced Information Systems Engineering (CAiSE) <32, 2020, Grenoble>
English
Conference Paper
Fraunhofer FIT ()

Abstract
Conformance checking techniques let us find out to what degree a process model and real execution data correspond to each other. In recent years, alignments have proven extremely useful in calculating conformance statistics. Most techniques to compute alignments provide an exact solution. However, in many applications, it is enough to have an approximation of the conformance value. Specifically, for large event data, the computation time for alignments is considerably long using current techniques which makes them inapplicable in reality. Also, it is no longer feasible to use standard hardware for complex process models. This paper, proposes new approximation techniques to compute approximated conformance checking values close to exact solution values in less time. These methods also provide upper and lower bounds for the approximated alignment value. Our experiments on real event data show that it is possible to improve the performance of conformance checking by using the proposed methods compared to using the state-of-the-art alignment approximation technique. Results show that in most of the cases, we provide tight bounds, accurate approximated alignment values, and similar deviation statistics.

: http://publica.fraunhofer.de/documents/N-596039.html