Data are a central phenomenon in our digital information age. They impact the way we live, work, and play and provide unprecedented opportunities to simplify our daily life and behavior. They implicate enormous potential and impact society, economy, and science. Due to the advancement of cyber-physical systems and Internet of Things technologies, it is expected that the majority of real-time data will be generated from devices interconnected within the Internet of Things by the year 2025. In this paper, we tackle the problem of managing Internet of Things data in an efficient way. To this end, we introduce the metric approach for storing and querying Internet of Things data and investigate the ability of pivot-based tables for indexing and searching this type of data. Along with the introduction of two real-world, large-scale Internet of Things datasets from the EU projects COMPOSITION and MONSOON (under grant no. 723145 and 723650), we show that the metric approach facilitates efficient data access in the Internet of Things.

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The proliferation of cyber-physical systems and the advancement of Internet of Things technologies have led to an explosive digitization of the industrial sector. Driven by the high-tech strategy of the federal government in Germany, many manufacturers across all industry segments are accelerating the adoption of cyber-physical system and Internet of Things technologies to manage and ultimately improve their industrial production processes. In this work, we are focusing on the EU funded project MONSOON, which is a concrete example where production processes from different industrial sectors are to be optimized via data-driven methodology. We show how the particular problem of waste quantity reduction can be enhanced by means of machine learning. The results presented in this paper are useful for researchers and practitioners in the field of machine learning for cyber-physical systems in data-intensive Industry 4.0 domains.

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