Process Mining Approach Based on Partial Structures of Event Logs and Decision Tree Learning

Abstract

Process mining techniques are able to improve processes by extracting knowledge from event logs commonly available in today's information systems. In the area, it is important to verify whether business goals can be satisfied. LTL (Linear Temporal Logic) verification is an important means for checking the goals automatically and exhaustively. However, writing formal language like LTL is difficult, and the properties by which the user's intentions are not reflected sufficiently have bad influence on the verification results. Therefore, it is needed to help writing correct LTL formula for users who do not have sufficient domain knowledge and knowledge of mathematical logic. We propose an approach for goal achievement prediction based on decision tree learning. It is conducted focusing on partial structures represented as event order relations of each trace. The proposed technique is evaluated on a phone repair process log.