Towards Bridging the Gap Between Model- and Data- Driven Tool Suites for Cyber-Physical Systems

Model-driven approaches in developing and operating Cyber-Physical Systems are increasingly complemented by data-driven methods. Examples for their use cases are the analysis of model repositories for discovering patterns and relationships in models, the design-time learning of approximate system and environment models from data, and the detection of divergence from design-time assumptions during operations. In this paper we argue that model-and data-driven approaches have combined use cases that that need complementary services provided by modeling and data analytic frameworks. However, convergence of model-driven and data-driven methods is hindered by the strongly different tool infrastructure. The paper summarizes the integration challenges and proposes a semantic bridge as a solution for filling the gap between the model - and data-driven tool suites.