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).
    @inproceedings{89c65fc1b1aa47f4b3d0008b111e4c26,
    title = "Mining Staff Assignment Rules from Event-Based Data",
    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.",
    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",
    author = "Ly, {Linh Thao} and Stefanie Rinderle and Peter Dadam and Manfred Reichert",
    year = "2006",
    month = "2",
    day = "15",
    doi = "10.1007/11678564_16",
    language = "English",
    isbn = "978-3-540-32595-6",
    series = "Lecture Notes in Computer Science",
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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

    TY - GEN

    T1 - Mining Staff Assignment Rules from Event-Based Data

    AU - Ly, Linh Thao

    AU - Rinderle, Stefanie

    AU - Dadam, Peter

    AU - Reichert, Manfred

    PY - 2006/2/15

    Y1 - 2006/2/15

    N2 - 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.

    AB - 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.

    KW - SCS-Services

    KW - Quality of Service (QoS)

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    KW - Workflow

    KW - Business process engineering

    KW - Business process management

    KW - Classification

    KW - Collaboration

    KW - Electronic commerce

    KW - Information Systems

    KW - Management

    KW - Modeling

    KW - Performance

    KW - Risk management

    KW - Semantic web

    KW - Simulation

    U2 - 10.1007/11678564_16

    DO - 10.1007/11678564_16

    M3 - Conference contribution

    SN - 978-3-540-32595-6

    T3 - Lecture Notes in Computer Science

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    EP - 190

    BT - Business Process Management Workshops

    A2 - Bussler, Christoph J.

    A2 - Haller, Armin

    PB - Springer

    CY - Berlin, Heidelberg

    ER -

    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