  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?"
