The ROAD from sensor data to process instances via interaction mining

Abstract

Process mining is a rapidly developing field that aims at automated modeling of business processes based on data coming from event logs. In recent years, advances in tracking technologies, e.g., Real-Time Locating Systems (RTLS), put forward the ability to log business process events as location sensor data. To apply process mining techniques to such sensor data, one needs to overcome an abstraction gap, because location data recordings do not relate to the process directly. In this work, we solve the problem of mapping sensor data to event logs based on process knowledge. Specifically, we propose interactions as an intermediate knowledge layer between the sensor data and the event log. We solve the mapping problem via optimal matching between interactions and process instances. An empirical evaluation of our approach shows its feasibility and provides insights into the relation between ambiguities and deviations from process knowledge, and accuracy of the resulting event log.