Analyzing Data Streams from Cyber-Physical-Systems: A Case Study

SN Computer Science, vol. 5

Abstract

Abstract We show that conducting a process-mining-centric analysis concerning cyber-physical systems provides insights into usage behavior. To show that, we perform our analysis on connected-vehicle data. We transform connected-vehicle data into an event log. We analyze the resulting event log using various process-mining techniques. In particular, we apply basic statistical analysis as well as process-discovery and conformance-checking techniques to receive a well-representative process model. We apply various process-enhancement techniques to get deeper insights. Finally, we capture a multi-perspective view using a state-based approach. We show deviations between a de-jure model and our picked process model, leading to better knowledge concerning real user behavior. We observed that the predefined escalation of warning states does not happen. Additionally, we verified system requirements. Furthermore, we show that the reasons for drivers’ behavior are not related to system issues. Applying process-mining techniques to data concerning cyber-physical systems provides valuable insights into their functionality in a real-world setting. By utilizing process-mining techniques, we can extract insights to a human-understandable level and provide a well-studied access point.

Authors 5

1. Harry H. Beyel corresponding Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

2. Omar Makke

   Ford Motor Company (United States)

   Affiliation as printed

   Global Data Insight & Analytics, Ford, Dearborn, USA

3. Mahsa Pourbafrani Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

4. Oleg Gusikhin

   Ford Motor Company (United States)

   Affiliation as printed

   Global Data Insight & Analytics, Ford, Dearborn, USA

5. Wil M. P. van der Aalst Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany