Abstract:
Process Aware Information Systems (PAIS) are IT systems which support
business processes and generate event-logs as a result of execution of the
supported business processes. Fuzzy-Miner (FM) is a popular algorithm
within Process Mining which consists of discovering a process model from
the event-logs. In traditional FM algorithm, the extracted process model
consists of nodes and edges of equal value. However, in real-world applications,
the actors, activities and transition between activities may not be of
equal value. In this paper, we propose a utility-based Fuzzy Miner (UBFM)
algorithm to e ciently mine a process model driven by a utility threshold.
The term utility can be measured in terms of pro t, value, quantity or
other expressions of user's preference. The focus of this paper is to take
into consideration the statistical (based on frequency) and semantic (based
on user's objective) aspect into account. We conduct experiments on realworld
dataset and demonstrate the effectiveness of our approach.