A framework for learning and embedding multi-sensor forecasting models into a decision support system: A case study of methane concentration in coal mines

We introduce a new approach for learning forecasting models over large multi-sensor data sets, including the steps of sliding-window-based feature extraction and rough-set-inspired feature subset ensemble selection. We show how to integrate this approach with the major data-processing-related components of DISESOR - a decision support system which is a coherent and complete framework for exploring streams of sensor readings registered in underground coal mines. As a case study, we report our experiments related to the task of methane concentration forecasting. The contributions in this paper refer to both the analysis how the nature of sensor readings influenced the architecture of the developed system and the empirical proof that the designed methods for data processing and analytics turned out to be efficient in practice.