A Hierarchical Markov Model to Understand the Behaviour of Agents in Business Processes
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.
KeywordsProcess Mining Agent-Based Simulation Markov Models Expectation-Maximization Agent-Object-Relationship (AOR)
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