A generic framework for context-aware process performance analysis


Hompes, B.F.A., Buijs, J.C.A.M. & van der Aalst, W.M.P. (2016). A generic framework for context-aware process performance analysis. In E. Kuhn, T. Dillon, H. Panetto, C. Agostino Ardagna , R. Meersman, G. O'Sullivan & C. Debruyne (Eds.), On the Move to Meaningful Internet Systems: OTM 2016 Conferences (pp. 300-317). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10033 LNCS). Dordrecht: Springer. In Scopus Cited 2 times.

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Process mining combines model-based process analysis with data-driven analysis techniques. The role of process mining is to extract knowledge and gain insights from event logs. Most existing techniques focus on process discovery (the automated extraction of process models) and conformance checking (aligning observed and modeled behavior). Relatively little research has been performed on the analysis of business process performance. Cooperative business processes often exhibit a high degree of variability and depend on many factors. Finding root causes for inefficiencies such as delays and long waiting times in such flexible processes remains an interesting challenge. This paper introduces a novel approach to analyze key process performance indicators by considering the process context. A generic context-aware analysis framework is presented that analyzes performance characteristics from multiple perspectives. A statistical approach is then utilized to evaluate and find significant differences in the results. Insights obtained can be used for finding high-impact points for optimization, prediction, and monitoring. The practical relevance of the approach is shown in a case study using real-life data.