Narrowing the business-IT gap in process performance measurement

H. Van Der Aa, A. Del-Río-Ortega, M. Resinas, H. Leopold, A. Ruiz-Cortés, J. Mendling, H.A. Reijers

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

    7 Citations (Scopus)
    1 Downloads (Pure)


    To determine whether strategic goals are met, organizations must monitor how their business processes perform. Process Performance Indicators (PPIs) are used to specify relevant performance requirements. The formulation of PPIs is typically a managerial concern. Therefore, considerable effort has to be invested to relate PPIs, described by management, to the exact operational and technical characteristics of business processes. This work presents an approach to support this task, which would otherwise be a laborious and time-consuming endeavor. The presented approach can automatically establish links between PPIs, as formulated in natural language, with operational details, as described in process models. To do so, we employ machine learning and natural language processing techniques. A quantitative evaluation on the basis of a collection of 173 real-world PPIs demonstrates that the proposed approach works well.

    Original languageEnglish
    Title of host publicationAdvanced Information Systems Engineering
    Subtitle of host publication28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings
    EditorsS. Nurcan, P. Soffer, M. Bajec, J. Eder
    Place of PublicationDordrecht
    Number of pages15
    ISBN (Electronic)978-3-319-39696-5
    ISBN (Print)978-3-319-39695-8
    Publication statusPublished - 2016
    Event28th International Conference on Advanced Information Systems Engineering (CAiSE 2016) - Ljubljana, Slovenia
    Duration: 13 Jun 201617 Jun 2016
    Conference number: 28

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print)03029743
    ISSN (Electronic)16113349


    Conference28th International Conference on Advanced Information Systems Engineering (CAiSE 2016)
    Abbreviated titleCAiSE '16
    Other"Information Systems for Connecting People"
    Internet address


    • Model alignment
    • Natural language processing
    • Performance measurement
    • Process performance indicators


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