One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain non-trivial constructs, processes that are low structured and/or dealing with the presence of noise in the event logs. To overcome these problems, a new process representation language
(i.e. augmented Causal nets) is presented in combination with an accompanying process mining algorithm. The most signficant property of the new representation language is in the way the semantics of splits and joins are represented; by using so-called split/join frequency tables. This result in easy to understand process models even in the case of non-trivial constructs, low structured domains and the presence of noise. The new
process representation language and mining technique can also be used for conformance checking; to indicate if all the behavior in the event log is also represented in the process model and if there is extra behavior in the process model not in the event log. This paper explains the new process representation language and how the mining algorithm works. The algorithm is implemented as a plug-in in the ProM framework. An illustrative example with noise and a real life log of a complex and low structured process are used to explicate the presented approach.
|Place of Publication||Eindhoven|
|Publisher||Technische Universiteit Eindhoven|
|Number of pages||24|
|Publication status||Published - 2011|
|Name||BETA publicatie : working papers|