Article

Detecting implicit dependencies between tasks from event logs

Authors:

Jiaguang SunAuthors Info & Claims

APWeb'06: Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development

Pages 591 - 603

https://doi.org/10.1007/11610113_52

Published: 16 January 2006 Publication History

Abstract

Process mining aims at extracting information from event logs to capture the business process as it is being executed. In spite of many researchers’ persistent efforts, there are still some challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, where the process models are represented in Petri nets. In fact, there are totally two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. Implicit dependency is very hard to mine by current mining approaches. Thus we propose three theorems to detect implicit dependency between tasks and give their proofs. The experimental results show that our approach is powerful enough to mine process models with non-free-choice constructs.

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Cited By

Tapia-Flores TLópez-Mellado E(2024)Discovering workflow nets of concurrent iterative processesActa Informatica10.1007/s00236-023-00445-561:1(1-21)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00236-023-00445-5
Alkofahi HUmphress DAlawneh H(2022)Discovering Conditional Business Rules in Web Applications Using Process MiningInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_7(90-97)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-21047-1_7
Ferilli SAngelastro S(2019)Activity prediction in process mining using the WoMan frameworkJournal of Intelligent Information Systems10.1007/s10844-019-00543-253:1(93-112)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10844-019-00543-2
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Index Terms

Detecting implicit dependencies between tasks from event logs
1. Applied computing
  1. Enterprise computing
    1. Business process management

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Guide Proceedings

APWeb'06: Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development

January 2006

1217 pages

ISBN:3540311424

Editors:
Xiaofang Zhou
School of ITEE, The University of Queensland, Australia
,
Jianzhong Li
School of Computer Science and Technology, Heilongjiang University, China
,
Heng Tao Shen
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
,
Masaru Kitsuregawa
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, QLD, Japan
,
Yanchun Zhang
Institute of Industrial Science, Victoria University, 4-6-1 Komaba, Meguro-ku, Tokyo, QLD, Australia

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 16 January 2006

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Cited By

Tapia-Flores TLópez-Mellado E(2024)Discovering workflow nets of concurrent iterative processesActa Informatica10.1007/s00236-023-00445-561:1(1-21)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00236-023-00445-5
Alkofahi HUmphress DAlawneh H(2022)Discovering Conditional Business Rules in Web Applications Using Process MiningInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_7(90-97)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-21047-1_7
Ferilli SAngelastro S(2019)Activity prediction in process mining using the WoMan frameworkJournal of Intelligent Information Systems10.1007/s10844-019-00543-253:1(93-112)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10844-019-00543-2
Geist VNatschläger CIllibauer CSchewe K(2018)Towards functional safety and security for adaptive and flexible business processesJournal of Software: Evolution and Process10.1002/smr.195230:5Online publication date: 17-May-2018
https://dl.acm.org/doi/10.1002/smr.1952
Zhu TGuo YJu AMa JWang X(2017)An Insider Threat Detection Method Based on Business Process MiningInternational Journal of Business Data Communications and Networking10.4018/ijbdcn.201707010713:2(83-98)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.4018/ijbdcn.2017070107
Tax NSidorova NHaakma RAalst W(2016)Log-based Evaluation of Label Splits for Process ModelsProcedia Computer Science10.1016/j.procs.2016.08.09696:C(63-72)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.08.096
Carolis BFerilli SRedavid D(2015)Incremental Learning of Daily Routines as Workflows in a Smart Home EnvironmentACM Transactions on Interactive Intelligent Systems10.1145/26750634:4(1-23)Online publication date: 28-Jan-2015
https://dl.acm.org/doi/10.1145/2675063
Huang ZKumar A(2012)A Study of Quality and Accuracy Trade-offs in Process MiningINFORMS Journal on Computing10.1287/ijoc.1100.044424:2(311-327)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1287/ijoc.1100.0444
Folino FGreco GGuzzo APontieri LDesai BSacca DGreco S(2009)Discovering expressive process models from noised log dataProceedings of the 2009 International Database Engineering & Applications Symposium10.1145/1620432.1620449(162-172)Online publication date: 16-Sep-2009
https://dl.acm.org/doi/10.1145/1620432.1620449
da Cruz JRuiz DFilho JGomes RGerosa MGuizzardi R(2008)Uma experiência em mineração de processos de manutenção de softwareCompanion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web10.1145/1809980.1810047(247-253)Online publication date: 26-Oct-2008
https://dl.acm.org/doi/10.1145/1809980.1810047
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