Mining local process models and their correlations

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Abstract

Mining local patterns of process behavior is a vital tool for the analysis of event data that originates from flexible processes, which in general cannot be described by a single process model without overgeneralizing the allowed behavior. Several techniques for mining local patterns have been developed over the years, including Local Process Model (LPM) mining, episode mining, and the mining of frequent subtraces. These pattern mining techniques can be considered to be orthogonal, i.e., they provide different types of insights on the behavior observed in an event log. In this work, we demonstrate that the joint application of LPM mining and other patter mining techniques provides benefits over applying only one of them. First, we show how the output of a subtrace mining approach can be used to mine LPMs more efficiently. Secondly, we show how instances of LPMs can be correlated together to obtain larger LPMs, thus providing a more comprehensive overview of the overall process. We demonstrate both effects on a collection of real-life event logs.

Original languageEnglish
Title of host publicationData-Driven Process Discovery and Analysis - 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers
EditorsMaurice van Keulen, Paolo Ceravolo, Kilian Stoffel
Place of PublicationCham
PublisherSpringer
Pages65-88
Number of pages24
ISBN (Electronic)978-3-030-11638-5
ISBN (Print)978-3-030-11637-8
DOIs
Publication statusPublished - 1 Jan 2019
Event7th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017 - Neuchatel, Switzerland
Duration: 6 Dec 20178 Dec 2017

Publication series

NameLecture Notes in Business Information Processing
Volume340
ISSN (Print)1865-1348

Conference

Conference7th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017
CountrySwitzerland
CityNeuchatel
Period6/12/178/12/17

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Genga, L., Tax, N., & Zannone, N. (2019). Mining local process models and their correlations. In M. van Keulen, P. Ceravolo, & K. Stoffel (Eds.), Data-Driven Process Discovery and Analysis - 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers (pp. 65-88). (Lecture Notes in Business Information Processing; Vol. 340). Cham: Springer. https://doi.org/10.1007/978-3-030-11638-5_4
Genga, Laura ; Tax, Niek ; Zannone, Nicola. / Mining local process models and their correlations. Data-Driven Process Discovery and Analysis - 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers. editor / Maurice van Keulen ; Paolo Ceravolo ; Kilian Stoffel. Cham : Springer, 2019. pp. 65-88 (Lecture Notes in Business Information Processing).
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Genga, L, Tax, N & Zannone, N 2019, Mining local process models and their correlations. in M van Keulen, P Ceravolo & K Stoffel (eds), Data-Driven Process Discovery and Analysis - 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers. Lecture Notes in Business Information Processing, vol. 340, Springer, Cham, pp. 65-88, 7th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017, Neuchatel, Switzerland, 6/12/17. https://doi.org/10.1007/978-3-030-11638-5_4

Mining local process models and their correlations. / Genga, Laura; Tax, Niek; Zannone, Nicola.

Data-Driven Process Discovery and Analysis - 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers. ed. / Maurice van Keulen; Paolo Ceravolo; Kilian Stoffel. Cham : Springer, 2019. p. 65-88 (Lecture Notes in Business Information Processing; Vol. 340).

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

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Genga L, Tax N, Zannone N. Mining local process models and their correlations. In van Keulen M, Ceravolo P, Stoffel K, editors, Data-Driven Process Discovery and Analysis - 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers. Cham: Springer. 2019. p. 65-88. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-030-11638-5_4