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International Conference on Innovative Techniques and Applications of Artificial Intelligence

SGAI 2018: Artificial Intelligence XXXV pp 132–142Cite as

Dynamic Process Workflow Routing Using Deep Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11311))

Abstract

Dynamic business processes are challenged by constant changes due to unstable environments, unexpected incidents and difficult to predict behaviours. In industry areas like customer support, complex incidents can be regarded as instances of a dynamic process since there can be no static planning against their unique nature. Support engineers will work with any means at their disposal to solve any emerging case and define a custom prioritization strategy, to achieve the best possible result. To assist with this, in this paper we describe a novel workflow application to address the tasks of high solution accuracy and shorter prediction resolution time. We describe how workflows can be generated to assist experts and how our solution can scale over time to produce domain-specific reusable cases for similar problems. Our work is evaluated using data from 5000 workflows from the automotive industry.

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Author information

Authors and Affiliations

  1. German Research Center for Artificial Intelligence, Smart Data and Knowledge Services, Kaiserslautern, Germany

    Kareem Amin, Klaus-Dieter Althoff & Andreas Dengel

  2. Institute of Computer Science, Intelligent Information Systems Lab, University of Hildesheim, Hildesheim, Germany

    Klaus-Dieter Althoff

  3. Kaiserslautern University, Kaiserslautern, Germany

    Kareem Amin & Andreas Dengel

  4. School of Computing Engineering and Mathematics, University of Brighton, Brighton, UK

    Stelios Kapetanakis

  5. Department of Computing, Middlesex University, London, UK

    Miltos Petridis

Authors
  1. Kareem Amin
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  2. Stelios Kapetanakis
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  3. Klaus-Dieter Althoff
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  4. Andreas Dengel
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  5. Miltos Petridis
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Corresponding author

Correspondence to Stelios Kapetanakis .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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Amin, K., Kapetanakis, S., Althoff, KD., Dengel, A., Petridis, M. (2018). Dynamic Process Workflow Routing Using Deep Learning. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-04191-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04190-8

  • Online ISBN: 978-3-030-04191-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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