Hompes, Bart F.A. & van der Aalst, Wil M.P. (2018). Lifecycle-Based Process Performance Analysis. In Dumitru Roman, Henderik A. Proper, Robert Meersman, Hervé Panetto, Christophe Debruyne & Claudio Agostino Ardagna (Eds.), On the Move to Meaningful Internet Systems. OTM 2018 Conferences – Confederated International Conferences (pp. 336-353). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 11229 LNCS). Springer.
Many business processes are supported by information systems that record their execution. Process mining techniques extract knowledge and insights from such process execution data typically stored in event logs or streams. Most process mining techniques focus on process discovery (the automated extraction of process models) and conformance checking (aligning observed and modeled behavior). Existing process performance analysis techniques typically rely on ad-hoc definitions of performance. This paper introduces a novel comprehensive approach to process performance analysis from event data. Our generic technique centers around business artifacts, key conceptual entities that behave according to state-based transactional lifecycle models. We present a formalization of these concepts as well as a structural approach to calculate and monitor process performance from event data. The approach has been implemented in the open source process mining tool ProM and its applicability has been evaluated using public real-life event data.