By expressing physical laws and control strategies, interoperable physical system models such as Functional Mock-up Units(FMUs) are playing a major role in designing, simulating, and evaluating complex (cyber-)physical systems. However, existing FMU simulation software environments require significant user/developer effort when such models need to be tightly integrated with actual data from a database and/or model simulation results need to be stored in a database, e.g., as a part of larger user analytical workflows. Hence, users encounter substantial complexity and overhead when using such physical models to solve analytical problems based on real data. To address this issue, this paper proposes pgFMU - an extension to the relational database management system PostgreSQL for integrating and conveniently using FMU-based physical models inside a database environment. pgFMU reduces the complexity in specifying (and executing) analytical workflows based on such simulation models(requiring on average 22x fewer code lines) while maintaining improved overall execution performance (up to 8.43x faster formulti-instance scenarios) due to the optimization techniques and integration between database and an FMU library. With pgFMU,cyber-physical data scientists are able to develop a typical FMUworkflow up to 11.74x faster than using the standard FMU software stack. When combined with an existing in-DBMS analytics tool, pgFMU can increase the accuracy of Machine Learning models by up to 21.1%.
|Conference||23rd International Conference on Extending Database Technology, EDBT 2020|
|Period||30/03/2020 → 02/04/2020|
|Series||Advances in Database Technology|