IDEAS home Printed from https://ideas.repec.org/a/spr/binfse/v60y2018i1d10.1007_s12599-018-0517-5.html
   My bibliography  Save this article

Process Modeling Recommender Systems

Author

Listed:
  • Michael Fellmann

    (University of Rostock)

  • Dirk Metzger

    (Osnabrück University)

  • Sven Jannaber

    (Osnabrück University)

  • Novica Zarvic

    (Osnabrück University)

  • Oliver Thomas

    (Osnabrück University)

Abstract

The manual construction of business process models is a time-consuming, error-prone task and presents an obstacle to business agility. To facilitate the construction of such models, several modeling support techniques have been suggested. However, while recommendation systems are widely used, e.g., in e-commerce, these techniques are rarely implemented in process modeling tools. The creation of such systems is a complex task since a large number of requirements and parameters have to be taken into account. In order to improve the situation, the authors have developed a data model that can serve as a backbone for the development of process modeling recommender systems (PMRS). This article outlines the systematic development of this model in a stepwise approach using established requirements and validates it against a data model that has been reverse-engineered from a real-world system. In a last step, the paper illustrates an exemplary instantiation of the data model in a Smart Glasses-based modeling environment and discusses business process agility issues. The authors expect their contribution to provide a useful starting point for designing the data perspective of process modeling recommendation features that support business agility in process-intensive environments.

Suggested Citation

  • Michael Fellmann & Dirk Metzger & Sven Jannaber & Novica Zarvic & Oliver Thomas, 2018. "Process Modeling Recommender Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(1), pages 21-38, February.
  • Handle: RePEc:spr:binfse:v:60:y:2018:i:1:d:10.1007_s12599-018-0517-5
    DOI: 10.1007/s12599-018-0517-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12599-018-0517-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12599-018-0517-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Oliver Thomas & Michael Fellmann M.A., 2009. "Semantic Process Modeling – Design and Implementation of an Ontology-based Representation of Business Processes," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 1(6), pages 438-451, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. A. Ryzhko L. & А. Рыжко Л., 2018. "Каузальная классификация бизнес-процессов предприятия // Causal Сlassification of Enterprise Business," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 8(1), pages 90-99.
    2. Jörg Becker & Patrick Delfmann & Hanns-Alexander Dietrich & Matthias Steinhorst & Mathias Eggert, 2016. "Business process compliance checking – applying and evaluating a generic pattern matching approach for conceptual models in the financial sector," Information Systems Frontiers, Springer, vol. 18(2), pages 359-405, April.
    3. José González Vázquez & Jürgen Sauer & Hans-Jürgen Appelrath, 2012. "Methods to Manage Information Sources for Software Product Managers in the Energy Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(1), pages 3-14, February.
    4. Frederik Gailly & Nadejda Alkhaldi & Sven Casteleyn & Wouter Verbeke, 2017. "Recommendation-Based Conceptual Modeling and Ontology Evolution Framework (CMOE+)," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(4), pages 235-250, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:binfse:v:60:y:2018:i:1:d:10.1007_s12599-018-0517-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.