Analyzing Behavior in Cyber-Physical Systems in Connected Vehicles: A Case Study

Cyber-physical systems embedded in various areas connected to the internet generate unprecedented volumes of data. Humans frequently interact with these systems, thus allowing companies to analyze the data and gain valuable insights into user-product interactions. To analyze the underlying behavior recorded in data, process-mining techniques can be used. However, to apply process mining, the low-level measurements have to be transformed into an event log. In this work, we analyze enriched and transformed connected-vehicle data dealing with an assistance system using process-mining techniques. We analyze the time spent in states of the system, compare behavior between different models using Kruskal-Wallis’ and Dunn’s test, and discover reasons for state switching. We demonstrate how companies can apply process mining on data collected by internet-of-things devices to understand the usage, ratify system requirements, and develop their products.