A Hierarchical Markov Model to Understand the Behaviour of Agents in Business Processes

  • Diogo R. Ferreira
  • Fernando Szimanski
  • Célia Ghedini Ralha
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 132)

Abstract

Process mining techniques are able to discover process models from event logs but there is a gap between the low-level nature of events and the high-level abstraction of business activities. In this work we present a hierarchical Markov model together with mining techniques to discover the relationship between low-level events and a high-level description of the business process. This can be used to understand how agents perform activities at run-time. In a case study experiment using an agent-based simulation platform (AOR), we show how the proposed approach is able to discover the behaviour of agents in each activity of a business process for which a high-level model is known.

Keywords

Process Mining Agent-Based Simulation Markov Models Expectation-Maximization Agent-Object-Relationship (AOR) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Diogo R. Ferreira
    • 1
  • Fernando Szimanski
    • 2
  • Célia Ghedini Ralha
    • 2
  1. 1.IST – Technical University of LisbonPortugal
  2. 2.Universidade de Brasília (UnB)Brazil

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