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


    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
    Number of pages14
    ISBN (Electronic)978-3-540-32596-3
    ISBN (Print)978-3-540-32595-6
    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
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference1st Workshop on Business Process Intelligence, BPI 2005
    Abbreviated titleBPI


    • 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


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