Analytics over Multi-sensor Time Series Data – A Case-Study on Prediction of Mining Hazards

Abstract

Mining of high-dimensional time series data that represent readings of multiple sensors is a challenging task. We focus on several important aspects of analytics over such data. We describe a methodology for extracting informative features from multidimensional data streams, as well as algorithms for finding compact representations of such data, in order to facilitate the construction of prediction models. We pay special attention to designing new approaches to dimensionality reduction and interchangeability of features that such representations comprise of. We validate our algorithms on data sets obtained from coal mines and we demonstrate how their results can be applied for a construction of a decision support system. We show that such system is efficient and that its outcomes can be easily interpreted by subject matter experts.