Robust drift characterization from event streams of business processes
AuthorOstovar, A; Leemans, SJJ; Rosa, ML
Source TitleACM Transactions on Knowledge Discovery from Data
PublisherAssociation for Computing Machinery (ACM)
University of Melbourne Author/sLa Rosa, Marcello
AffiliationComputing and Information Systems
Document TypeJournal Article
CitationsOstovar, A., Leemans, S. J. J. & Rosa, M. L. (2020). Robust drift characterization from event streams of business processes. ACM Transactions on Knowledge Discovery from Data, 14 (3), pp.1-57. https://doi.org/10.1145/3375398.
Access StatusAccess this item via the Open Access location
Open Access URLPublished version
ARC Grant codeARC/DP150103356
Process workers may vary the normal execution of a business process to adjust to changes in their operational environment, e.g., changes in workload, season, or regulations. Changes may be simple, such as skipping an individual activity, or complex, such as replacing an entire procedure with another. Over time, these changes may negatively affect process performance; hence, it is important to identify and understand them early on. As such, a number of techniques have been developed to detect process drifts , i.e., statistically significant changes in process behavior, from process event logs (offline) or event streams (online). However, detecting a drift without characterizing it, i.e., without providing explanations on its nature, is not enough to help analysts understand and rectify root causes for process performance issues. Existing approaches for drift characterization are limited to simple changes that affect individual activities. This article contributes an efficient, accurate, and noise-tolerant automated method for characterizing complex drifts affecting entire process fragments. The method, which works both offline and online, relies on two cornerstone techniques, one to automatically discover process trees from event streams (logs) and the other to transform process trees using a minimum number of change operations. The operations identified are then translated into natural language statements to explain the change behind a drift. The method has been extensively evaluated on artificial and real-life datasets, and against a state-of-the-art baseline method. The results from one of the real-life datasets have also been validated with a process stakeholder.
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