Multiresolution Analytics for Large Scale Industrial Processes

Abstract

Data collected from Industry 4.0 scenarios present a variety of data structures, reflecting the evolution of industrial processes, measurement systems and IT infrastructures (“variety” is actually one of the 4 V’s of Big Data, meaning that its existence is widely recognized). Data analytics platforms must adapt to this context and keep the pace of its evolution, in order to continue providing effective solutions to practitioners for dealing with the large data resources now available. In this context, one prevalent feature of industrial data has been largely overlooked: their multiresolution nature. The multiresolution nature of data is directly connected to their granularity in the time domain, an aspect that induces inner dependencies that current frameworks cannot address in a consistent and rigorous way. Furthermore, multiresolution has been often mistaken as a simple multirate scenario, where in fact the meaning of the observations is completely different. In this paper, we highlight such differences and discuss current multiresolution frameworks for effectively handling industrial data sets.