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. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ferreira, D. R., Szimanski, F., & Ralha, C. G. (2013). A hierarchical Markov model to understand the behaviour of agents in business processes. In Lecture Notes in Business Information Processing (Vol. 132 LNBIP, pp. 150–161). Springer Verlag. https://doi.org/10.1007/978-3-642-36285-9_16
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