Modelling Multi-Component Predictive Systems as Petri Nets
Authors: Salvador, Budka, M. and Gabrys, B.
Editors: Kacprzyk, J. and Owsinski, J.
Start date: 31 May 2017
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 workflow 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 (i.e. not being comprehensible by human experts). In this paper, we adopt and suitably re-define the Well-handled with Regular Iterations Work Flow (WRI-WF) Petri nets to represent MCPSs. The use of such WRI-WF 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.