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Evaluating KIPN for Modeling KIP

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Business Process Management Workshops (BPM 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 171))

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Abstract

Modeling Knowledge-Intensive Processes (KIP) is not an easy task due to its essential characteristics: unstructured, based on social interactions, experience and intentions of the actors involved. The previously developed Knowledge Intensive Process Ontology (KIPO) proposed all the concepts (and relationships among them) to make a KIP explicit. However, KIPO does not provide a graphical notation, which is crucial for the stakeholders to reach a common understanding about it. This paper presents the Knowledge Intensive Process Notation (KIPN), a notation for building knowledge-intensive processes graphical models. We discuss the results of an exploratory study where a KIP was modeled by its participants. The goal was to explore the use of the notation as a basis for identifying the elements within the process modeling.

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Authors and Affiliations

  1. Research and Practice Group in Information Architecture (NP2Tec) Department of Applied Informatics Federal, University of the State of Rio de Janeiro (UNIRIO), Janeiro, Brazil

    Joanne Manhães Netto, Flávia Maria Santoro & Fernanda Araujo Baião

Authors
  1. Joanne Manhães Netto
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  2. Flávia Maria Santoro
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  3. Fernanda Araujo Baião
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Corresponding authors

Correspondence to Joanne Manhães Netto , Flávia Maria Santoro or Fernanda Araujo Baião .

Editor information

Editors and Affiliations

  1. University of Rostock, Rostock, Germany

    Niels Lohmann

  2. Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South Korea)

    Minseok Song

  3. Stockholm University, Stockholm, Sweden

    Petia Wohed

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© 2014 Springer International Publishing Switzerland

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Netto, J.M., Santoro, F.M., Baião, F.A. (2014). Evaluating KIPN for Modeling KIP. In: Lohmann, N., Song, M., Wohed, P. (eds) Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-319-06257-0_44

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  • DOI: https://doi.org/10.1007/978-3-319-06257-0_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06256-3

  • Online ISBN: 978-3-319-06257-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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