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Incorporating User Behavior Patterns to Discover Workflow Models from Event Logs

We propose a novel approach to discover workflow models from event logs. The proposed approach addresses two major limitations of current process mining approaches. First, they assume either a single workflow model for the entire event log or the availability of workflow ids that can be used to group logs associated with the same workflow model together. Nonetheless, these assumptions are oversimplified as a complex system typically runs multiple workflow models, all of which share the same log system. Second, existing process mining approaches do not consider the usage patterns of workflow users. Most systems support multi-users and each user is typically associated with (or use) certain number of operation sequences, which may all follow one or several workflow models. Hence, we propose to leverage User Behavior Patterns (or UBPs) to improve the outcome of process mining. In particular, we exploit machine learning techniques to incorporate UBPs into sequence clustering for workflow model discovery. We model a UBP as a probabilistic distribution on sequences, which allows to compute the distance between a UBP and any sequence. We apply three-way matrix factorization onto a UBP-sequence distance matrix to co-cluster users and sequences. In this way, users that share similar UBPs are grouped together while the clustering of similar sequences will lead to the discovery of workflow models. An comprehensive experimental study is conducted to demonstrate the effectiveness and efficiency of the proposed approach.