Process Discovery using Classification Tree Hidden Semi-Markov Model
This work addresses the need for analyzing event logs in information systems to understand underlying processes, though it appears incremental as it combines existing techniques.
The paper tackles the problem of learning process models from event sequence logs by proposing a probabilistic model that combines hidden semi-Markov models and classification trees, enabling the identification of frequent sequences of system dynamics relevant to observable events, such as predicting health condition patterns from medical treatments.
Various and ubiquitous information systems are being used in monitoring, exchanging, and collecting information. These systems are generating massive amount of event sequence logs that may help us understand underlying phenomenon. By analyzing these logs, we can learn process models that describe system procedures, predict the development of the system, or check whether the changes are expected. In this paper, we consider a novel technique that models these sequences of events in temporal-probabilistic manners. Specifically, we propose a probabilistic process model that combines hidden semi-Markov model and classification trees learning. Our experimental result shows that the proposed approach can answer a kind of question-"what are the most frequent sequence of system dynamics relevant to a given sequence of observable events?". For example, "Given a series of medical treatments, what are the most relevant patients' health condition pattern changes at different times?"