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Potentials of Image Mining for Business Process Management

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Intelligent Decision Technologies 2016

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 57))

Abstract

An enormous amount of data in the context of business processes is stored as images. They contain valuable information for business process management. Up to now this data had to be integrated manually into the business process. By advances of capturing it is possible to extract information from an increasing number of images. Therefore, we systematically investigate the potentials of Image Mining for business process management by a literature research and an in-depth analysis of the business process lifecycle. As a first step to evaluate our research, we developed a prototype for recovering process model information from drawings using Rapidminer.

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Author information

Authors and Affiliations

  1. Munich University of Applied Sciences, Munich, Germany

    Rainer Schmidt & Michael Möhring

  2. Aalen University of Applied Sciences, Aalen, Germany

    Ralf-Christian Härting & Barbara Keller

  3. Reutlingen University, Reutlingen, Germany

    Alfred Zimmermann

Authors
  1. Rainer Schmidt
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  2. Michael Möhring
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  3. Alfred Zimmermann
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  4. Ralf-Christian Härting
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  5. Barbara Keller
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Corresponding author

Correspondence to Rainer Schmidt .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, UK

    Robert J. Howlett

  4. University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Schmidt, R., Möhring, M., Zimmermann, A., Härting, RC., Keller, B. (2016). Potentials of Image Mining for Business Process Management. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies 2016. Smart Innovation, Systems and Technologies, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-39627-9_38

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  • DOI: https://doi.org/10.1007/978-3-319-39627-9_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39626-2

  • Online ISBN: 978-3-319-39627-9

  • eBook Packages: EngineeringEngineering (R0)

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