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International Conference on Business Process Management

BPM 2014: Business Process Management Workshops pp 477–488Cite as

Constructing Probabilistic Process Models Based on Hidden Markov Models for Resource Allocation

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 202))

Abstract

A Hidden Markov Model (HMM) is a temporal statistical model which is widely utilized for various applications such as gene prediction, speech recognition and localization prediction. HMM represents the state of the process in a discrete variable, where the values are the possible observations of the world. For the purpose of process mining for resource allocation, HMM can be applied to discover a probabilistic workflow model from activities and identify the observations based on the resources utilized by each activity. In this paper, we introduce a process discovery method that combines an organizational perspective with a probabilistic approach to address the resource allocation and improve the productivity of resource management, maximizing the likelihood of the model using the Expectation-Maximization procedure.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2012R1A1B4003505).

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Authors and Affiliations

  1. Department of Industrial and Management Systems Engineering, Kyung Hee University, 1 Seochen-Dong, Giheung-Gu, Yongin, Gyeonggi, Republic of Korea

    Berny Carrera & Jae-Yoon Jung

Authors
  1. Berny Carrera
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  2. Jae-Yoon Jung
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Corresponding author

Correspondence to Jae-Yoon Jung .

Editor information

Editors and Affiliations

  1. IBM Research – Haifa, Haifa, Israel

    Fabiana Fournier

  2. Vienna University of Economics and Business, Vienna, Austria

    Jan Mendling

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Carrera, B., Jung, JY. (2015). Constructing Probabilistic Process Models Based on Hidden Markov Models for Resource Allocation. In: Fournier, F., Mendling, J. (eds) Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-319-15895-2_41

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  • DOI: https://doi.org/10.1007/978-3-319-15895-2_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15894-5

  • Online ISBN: 978-3-319-15895-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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