Mining the low-level behaviour of agents in high-level business processes

Abstract

Currently there is a gap between the high level of abstraction at which business processes are modelled and the low level nature of the events that are recorded during process execution. When applying process mining techniques, it is possible to discover the logic behind low-level events but it is difficult to determine the relationship between those low-level events and the high-level activities in a given process model. In this work, we introduce a hierarchical Markov model to capture both the high-level behaviour of activities and the low-level behaviour of events. We also develop an expectation-maximisation technique to discover that kind of hierarchical model from a given event log and a high-level description of the business process. We use this technique to understand the behaviour of agents in business processes, from the control-flow perspective and from the organisational perspective as well. Using an agent-based simulation platform (AOR), we implemented a purchasing process and generated an event log in order to illustrate the benefits of the proposed approach and to compare the results with existing process mining techniques, namely the ones that are available in the ProM framework.