Modelling Multi-Component Predictive Systems as Petri Nets.

Abstract

Building reliable data-driven predictive systems requires a considerable amount of human effort, especially in the data preparation and cleaning phase. In many application domains, multiple preprocessing steps need to be applied in sequence, constituting a ‘workflow’ and facilitating reproducibility. The concatenation of such work- flow with a predictive model forms a Multi-Component Predictive System (MCPS). Automatic MCPS composition can speed up this process by taking the human out of the loop, at the cost of model transparency. In this paper, we adopt and suitably re-define the Wellhandled with Regular Iterations Work Flow (WRI-WF) Petri nets to represent MCPSs. The use of such WRIWF nets helps to increase the transparency of MCPSs required in industrial applications and make it possible to automatically verify the composed workflows. We also present our experience and results of applying this representation to model soft sensors in chemical production plants.