HybridAIR - Hybrid Approach to Intelligent Recommenders for Testbed DevOps

The automotive industry has long been at the forefront of advances in automation. Modern assembly lines and supply chains are almost completely automated. Recent advances in data science and digitization have led to intelligent systems where data across the heterogeneous system landscape is becoming a major source of innovation. The complex data-driven discipline of vehicle development is supported by complex cyber-physical systems of systems (CPSoS) manifested by vehicle testbeds. However, novel propulsion systems are forcing the automotive industry to act faster than ever before. Development efficiency is the key and includes not only rapid development, but also the economical use of resources. HybridAIR aims to significantly increase the efficiency of CPSoS operation by introducing intelligent systems in the form of self-adaptive explainable recommender systems for different stakeholders applied on testbeds. Problem to solve: How can we cope with a constantly increasing volume of data and ever-growing data exchange within a multi-stakeholder and heterogeneous IT landscape (H2H, H2M, M2M)? How can we profitably turn around the associated challenges to create a source of knowledge from the many (informal) data sources, which not only mitigates the increasing complexity but even further increases efficiency? More concrete, how can we provide explainable, stakeholder-specific, high-quality, and context-aware decision support for testbed developers and testbed operators (DevOps), based on existing, shared data sources enriched with contextual information in their respective fields? How to build a generic, reusable infrastructure portable to reach a broad user group, whose respective knowledge is profitably reinforced?