Change point detection and dealing with gradual and multi-order dynamics in process mining

J. Martjushev, R.P. Jagadeesh Chandra Bose, W.M.P. van der Aalst

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

16 Citations (Scopus)
1 Downloads (Pure)

Abstract

In recent years process mining techniques have matured. Provided that the process is stable and enough example traces have been recorded in the event log, it is possible to discover a high-quality process model that can be used for performance analysis, compliance checking, and prediction. Unfortunately, most processes are not in steady-state and process discovery techniques have problems uncovering "second-order dynamics" (i.e., the process itself changes while being analyzed). This paper describes an approach to discover a variety of concept drifts in processes. Unlike earlier approaches, we can discover gradual drifts and multi-order dynamics (e.g., recurring seasonal effects mixed with the effects of an economic crisis). We use a novel adaptive windowing approach to robustly localize changes (gradual or sudden). Our extensive evaluation (based on objective criteria) shows that the new approach is able to efficiently uncover a broad range of drifts in processes.
Original languageEnglish
Title of host publicationPerspectives in Business Informatics Research
Subtitle of host publication14th International Conference, BIR 2015, Tartu, Estonia, August 26-28, 2015, Proceedings
EditorsR. Matulevicius, M. Dumas
Place of PublicationDordrecht
PublisherSpringer
Pages161-178
ISBN (Electronic)978-3-319-21915-8
ISBN (Print)978-3-319-21914-1
DOIs
Publication statusPublished - 2015

Publication series

NameLecture Notes in Business Information Processing
Volume229
ISSN (Print)1865-1348

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