Mining Staff Assignment Rules from Event-Based Data

Linh Thao Ly, Stefanie Rinderle, Peter Dadam, Manfred Reichert

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

Abstract

Process mining offers methods and techniques for capturing process behaviour from log data of past process executions. Although many promising approaches on mining the control flow have been published, no attempt has been made to mine the staff assignment situation of business processes. In this paper, we introduce the problem of mining staff assignment rules using history data and organisational information (e.g., an organisational model) as input. We show that this task can be considered an inductive learning problem and adapt a decision tree learning approach to derive staff assignment rules. In contrast to rules acquired by traditional techniques (e.g., questionnaires) the thus derived rules are objective and show the staff assignment situation at hand. Therefore, they can help to better understand the process. Moreover, the rules can be used as input for further analysis, e.g., workload balance analysis or delta analysis. This paper presents the current state of our work and points out some challenges for future research.
Original languageEnglish
Title of host publicationBusiness Process Management Workshops
Subtitle of host publicationBPM 2005 International Workshops, BPI, BPD, ENEI, BPRM, WSCOBPM, BPS, Nancy, France, September 5, 2005. Revised Selected Papers
EditorsChristoph J. Bussler, Armin Haller
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages177-190
Number of pages14
ISBN (Electronic)978-3-540-32596-3
ISBN (Print)978-3-540-32595-6
DOIs
Publication statusPublished - 15 Feb 2006
Event1st Workshop on Business Process Intelligence, BPI 2005 - Nancy, France
Duration: 5 Sep 20055 Sep 2005
Conference number: 1

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume3812
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Workshop on Business Process Intelligence, BPI 2005
Abbreviated titleBPI
CountryFrance
CityNancy
Period5/09/055/09/05

Fingerprint

Decision trees
Flow control
Industry

Keywords

  • SCS-Services
  • Quality of Service (QoS)
  • Web services
  • Workflow
  • Business process engineering
  • Business process management
  • Classification
  • Collaboration
  • Electronic commerce
  • Information Systems
  • Management
  • Modeling
  • Performance
  • Risk management
  • Semantic web
  • Simulation

Cite this

Ly, L. T., Rinderle, S., Dadam, P., & Reichert, M. (2006). Mining Staff Assignment Rules from Event-Based Data. In C. J. Bussler, & A. Haller (Eds.), Business Process Management Workshops: BPM 2005 International Workshops, BPI, BPD, ENEI, BPRM, WSCOBPM, BPS, Nancy, France, September 5, 2005. Revised Selected Papers (pp. 177-190). (Lecture Notes in Computer Science; Vol. 3812). Berlin, Heidelberg: Springer. https://doi.org/10.1007/11678564_16
Ly, Linh Thao ; Rinderle, Stefanie ; Dadam, Peter ; Reichert, Manfred. / Mining Staff Assignment Rules from Event-Based Data. Business Process Management Workshops: BPM 2005 International Workshops, BPI, BPD, ENEI, BPRM, WSCOBPM, BPS, Nancy, France, September 5, 2005. Revised Selected Papers. editor / Christoph J. Bussler ; Armin Haller. Berlin, Heidelberg : Springer, 2006. pp. 177-190 (Lecture Notes in Computer Science).
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abstract = "Process mining offers methods and techniques for capturing process behaviour from log data of past process executions. Although many promising approaches on mining the control flow have been published, no attempt has been made to mine the staff assignment situation of business processes. In this paper, we introduce the problem of mining staff assignment rules using history data and organisational information (e.g., an organisational model) as input. We show that this task can be considered an inductive learning problem and adapt a decision tree learning approach to derive staff assignment rules. In contrast to rules acquired by traditional techniques (e.g., questionnaires) the thus derived rules are objective and show the staff assignment situation at hand. Therefore, they can help to better understand the process. Moreover, the rules can be used as input for further analysis, e.g., workload balance analysis or delta analysis. This paper presents the current state of our work and points out some challenges for future research.",
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Ly, LT, Rinderle, S, Dadam, P & Reichert, M 2006, Mining Staff Assignment Rules from Event-Based Data. in CJ Bussler & A Haller (eds), Business Process Management Workshops: BPM 2005 International Workshops, BPI, BPD, ENEI, BPRM, WSCOBPM, BPS, Nancy, France, September 5, 2005. Revised Selected Papers. Lecture Notes in Computer Science, vol. 3812, Springer, Berlin, Heidelberg, pp. 177-190, 1st Workshop on Business Process Intelligence, BPI 2005, Nancy, France, 5/09/05. https://doi.org/10.1007/11678564_16

Mining Staff Assignment Rules from Event-Based Data. / Ly, Linh Thao; Rinderle, Stefanie; Dadam, Peter; Reichert, Manfred.

Business Process Management Workshops: BPM 2005 International Workshops, BPI, BPD, ENEI, BPRM, WSCOBPM, BPS, Nancy, France, September 5, 2005. Revised Selected Papers. ed. / Christoph J. Bussler; Armin Haller. Berlin, Heidelberg : Springer, 2006. p. 177-190 (Lecture Notes in Computer Science; Vol. 3812).

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

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Ly LT, Rinderle S, Dadam P, Reichert M. Mining Staff Assignment Rules from Event-Based Data. In Bussler CJ, Haller A, editors, Business Process Management Workshops: BPM 2005 International Workshops, BPI, BPD, ENEI, BPRM, WSCOBPM, BPS, Nancy, France, September 5, 2005. Revised Selected Papers. Berlin, Heidelberg: Springer. 2006. p. 177-190. (Lecture Notes in Computer Science). https://doi.org/10.1007/11678564_16