IoT Analytics: Collect, Process, Analyze, and Present Massive Amounts of Operational Data - Research and Innovation Challenges
Internet-of-Things (IoT) Analytics refers to the process of transforming vast amounts of information from heterogeneous internet-connected objects, data sources and devices to business and application intelligence. This chapter presents the few challenges of IoT analytics, while at the same time pro...
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| creator | Barnaghi, Payam Bauer, Martin Biswas, Abdur Rahim Botterman, Maarten Cheng, Bin Cirillo, Flavio Dillinger, Markus Graux, Hans Hoseinitabatabaie, Seyed Amir Kovacs, Ernö Longo, Salvatore Nunna, Swaroop Paulin, Alois Venkatesha Prasad, R. R. Soldatos, John Thuemmler, Christoph Volk, Mojca |
| description | Internet-of-Things (IoT) Analytics refers to the process of transforming vast amounts of information from heterogeneous internet-connected objects, data sources and devices to business and application intelligence. This chapter presents the few challenges of IoT analytics, while at the same time providing insights in possible solutions, notably solutions that are being developed in the scope of IoT European Research Cluster community. It presents challenges and solutions associated with the collection and semantic unification of diverse data streams, which is one of the first prerequisite steps for analyzing IoT data sources. The chapter presents the challenges of IoT/BigData convergence and illustrates techniques for integrating IoT with cloud and BigData infrastructures. It provides insights associated with the practical application of IoT analytics in healthcare and social care, and presents an IoT analytics case for public safety. The chapter deals with the ever important policy issues, through presenting challenges and providing a perspective for solutions that are in-line with existing and emerging EU directives. |
| doi_str_mv | 10.1201/9781003337454-7 |
| format | Book Chapter |
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| title | IoT Analytics: Collect, Process, Analyze, and Present Massive Amounts of Operational Data - Research and Innovation Challenges |
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