IDEAS home Printed from https://ideas.repec.org/h/spr/prochp/978-3-319-70491-3_13.html
   My bibliography  Save this book chapter

A Reasoning Based Knowledge Model for Business Process Analysis

In: Digital Transformation of the Consulting Industry

Author

Listed:
  • Anne Füßl

    (Technische Universität Ilmenau)

  • Franz Felix Füßl

    (iTech Solutions—Internet Technology Franz Felix Füßl)

  • Volker Nissen

    (Technische Universität Ilmenau)

  • Detlef Streitferdt

    (Technische Universität Ilmenau)

Abstract

The article presents the ontology based knowledge model iKnow that can automatically draw conclusions and integrate aspects of machine learning. Due to the knowledge-intensive nature of the consulting industry, the abstract reasoning based knowledge model can be used specifically for knowledge processing and decision support within a consulting project. There is a multitude of potential applications for iKnow in the realm of consulting. Business process analysis was chosen as a pilot application, since many consulting projects in the problem analysis and problem solving phase, require a comprehensive knowledge of business processes. In this paper it is outlined how iKnow can be used for an automated analysis of business process models. We describe the basic structure of the knowledge model as a business process analyzing tool and present a suitable demonstration. It is worth mentioning that iKnow does not necessarily rely on log-files or other data input from process-supporting IT-systems. In this way, and through the generality of its ontology based structure and reasoning capabilities, it is far more broadly applicable than current process mining solutions.

Suggested Citation

  • Anne Füßl & Franz Felix Füßl & Volker Nissen & Detlef Streitferdt, 2018. "A Reasoning Based Knowledge Model for Business Process Analysis," Progress in IS, in: Volker Nissen (ed.), Digital Transformation of the Consulting Industry, pages 323-349, Springer.
    • Handle: RePEc:spr:prochp:978-3-319-70491-3_13
    DOI: 10.1007/978-3-319-70491-3_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    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:prochp:978-3-319-70491-3_13. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.