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The Internet of Things (IoT), which connects physical objects with the virtual world, is considered one of the key technologies that enable and drive digital transformation, as the ability of IoT devices to capture and transmit data over networks and connectivity creates vast amounts of data that is generating substantial benefits for organizations (Marjani et al., 2017). The growing number of sensors, actuators and tags used in various areas of daily life, business and industry play a central role in a variety of applications characterized by generic terms such as “Industry 4.0”, “Smart City” and “Smart Home” (Ben-Daya, Hassini, & Bahroun, 2019; Yassine, Singh, Hossain, & Muhammad, 2019). These describe complex fields of application that not only attempt to digitize and optimize existing business and industrial processes using smart devices, but also create entirely new business and consumer application scenarios (Adi, Anwar, Baig, & Zeadally, 2020). Economic analysts predict that by 2023, 30% of companies in various industries will fully deploy on-premise IoT technologies and that the size of the global IoT market will grow to $800 billion (Gartner, 2019; Lheureux et al., 2020).
With the increasing number of embedded sensors, actuators and things connected to the Internet, the amount of data generated by IoT devices is also growing rapidly. Today, this data is becoming a critical asset that provides valuable opportunities for companies to grow, innovate and sustain a competitive advantage (Garg & Garg, 2020; Siow, Tiropanis, & Hall, 2018). However, it also poses an immense challenge in terms of data management, storage and analysis. In this context, data analytics of IoT data plays a crucial role in today's IoT domains and will be even more relevant in the future (Adi et al., 2020). The main objective of IoT analytics is to generate knowledge and context from data streams generated by a large number of heterogeneous devices to enable various IoT applications (Yassine et al., 2019). IoT analytics is described as a process in which a large amount of IoT data is analyzed to uncover trends, patterns, correlations and valuable insights to support decision making at both strategic and operational levels (ur Rehman et al., 2019). Depending on the type of IoT applications and business requirements, such analysis can be performed either by humans or by artificial intelligence and machine learning (AI / ML) in real time or over a longer period of time (Gupta & Jain, 2020; Minteer, 2017).