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Advanced Analytics on Complex Industrial Data

Jurgen van den Hoogen; Stefan Bloemheuvel; Martin Atzmueller
In: Werner Liebregts; Willem-Jan van den Heuvel; Arjan van den Born. Data Science for Entrepreneurship: Principles and Methods for Data Engineering, Analytics, Entrepreneurship, and the Society. Pages 177-203, Springer, 2023.


Complex data requires advanced analytics methods. In both science and industry, with the ever-increasing amounts of rich and large datasets available, advanced data analytics capabilities are required. Depending on the task at hand, several different techniques and methods can be used to analyze complex data (e.g., multivariate time series, log data, multimodal sensor data). In this chapter, we introduce approaches and methods for advanced analytics on complex industrial data. In particular, we focus on three exemplary methods for modeling and analyzing complex data, i.e., analytics for fault diagnosis, graph signal processing, and pattern mining on networks and graphs. We introduce the general approaches and methods and discuss their implementation in detail for an extended context. From a data perspective, we cover both sequential, i.e., time series, and relational, i.e., graph and network data, also bridging between both by analyzing signals on graphs. Besides setting the stage for the important theoretical background and concepts, we outline, in particular, the perspective on industrial applications and provide specific examples of the application of the presented methods in real-world industrial contexts, i.e., using complex industrial data.