Process Discovery using Classification Tree Hidden Semi-Markov Model

TL;DR

This paper introduces a novel probabilistic process model combining hidden semi-Markov models and classification trees to analyze event logs, enabling understanding of system dynamics and predicting relevant process patterns.

Contribution

It presents a new technique that models event sequences in a temporal-probabilistic manner using combined hidden semi-Markov models and classification trees.

Findings

The approach effectively identifies frequent system dynamics sequences.

It can predict relevant process pattern changes based on observable events.

Experimental results demonstrate the model's applicability to real-world data.

Abstract

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?"