InfoPos: A ML-Assisted Solution Design Support Framework for Industrial Cyber-Physical Systems

The variety of building blocks and algorithms incorporated in data-centric and ML-assisted solutions is high, contributing to two challenges: selection of most effective set and order of building blocks, as well as achieving such a selection with minimum cost. Considering that ML-assisted solution design is influenced by the extent of available data, as well as available knowledge of the target system, it is advantageous to be able to select matching building blocks. We introduce the first iteration of our InfoPos framework, allowing the placement of use-cases considering the available positions (levels), i.e., from poor to rich, of knowledge and data dimensions. With that input, designers and developers can reveal the most effective corresponding choice(s), streamlining the solution design process. The results from our demonstrator, an anomaly identification use-case for industrial Cyber-Physical Systems, reflects achieved effects upon the use of different building blocks throughout knowledge and data positions. The achieved ML model performance is considered as the indicator. Our data processing code and the composed data sets are publicly available.