Discovery of effective infrequent sequences based on maximum probability path

Abstract

Process discovery usually analyses frequent behaviour in event logs to gain an intuitive understanding of processes. However, there are some effective infrequent behaviours that help to improve business processes in real life. Most existing studies either ignore them or treat them as harmful behaviours. To distinguish effective infrequent sequences from noisy activities, this paper proposes an algorithm to analyse the distribution states of activities and the strong transfer relationships between behaviours based on maximum probability paths. The algorithm divides episodic traces into two categories: harmful and useful episodes, namely noisy activities and effective sequences. First, using conditional probability entropy, the infrequent logs are pre-processed to remove individual noisy activities that are extremely irregularly distributed in the traces. Effective sequences are then extracted from the logs based on the state transfer information of the activities. The algorithm is based on a PM4Py implementation and is validated using synthetic and real logs. From the results, the algorithm not only preserves the key structure of the model and reduces noise activity, but also improves the quality of the model.

Entropy-Based Behavioral Closeness Filtering Chaotic Activity Method

“…Most existing research either ignores them or dismisses them as harmful chaotic behaviors. In order to distinguish between valid infrequent sequences and chaotic activities, an algorithm based on maximum probability path analysis of strong migration relationships between activity distribution states and behaviors has been proposed in [23]. Infrequent logs are preprocessed using conditional probabilistic entropy to remove individual noisy activities that are very irregularly distributed in the trajectory.…”