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Directed Acyclic Graph Extraction from Event Logs

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Information and Software Technologies (ICIST 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 465))

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Abstract

The usage of probabilistic models in business process mining enables analysis of business processes in a more efficient manner. Although, the Bayesian belief network is one of the most common probabilistic models, possibilities to use it in business process mining are still not widely researched. Existing process mining approaches are incapable to extract directed acyclic graphs for representing Bayesian networks. This paper presents an approach for extraction of directed acyclic graph from event logs. The results obtained during the experiment show that the proposed approach is feasible and may be applied in practice.

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Author information

Authors and Affiliations

  1. Information Systems Research Laborary, Vilnius Gediminas Technical University, SaulÄ—tekio al. 11, LT-10223, Lithuania

    Olegas Vasilecas, Titas Savickas & Evaldas Lebedys

Authors
  1. Olegas Vasilecas
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  2. Titas Savickas
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  3. Evaldas Lebedys
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Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Studentu g. 50-409a,, LT-51368, Kaunas, Lithuania

    Giedre Dregvaite

  2. Kaunas University of Technology, Studentu g. 50-409a, LT-51368, Kaunas, Lithuania

    Robertas Damasevicius

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Vasilecas, O., Savickas, T., Lebedys, E. (2014). Directed Acyclic Graph Extraction from Event Logs. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2014. Communications in Computer and Information Science, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-319-11958-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-11958-8_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11957-1

  • Online ISBN: 978-3-319-11958-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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