Reinforcement Learning for Predictive Analytics in Smart Cities
AbstractThe digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller (
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Kolomvatsos, K.; Anagnostopoulos, C. Reinforcement Learning for Predictive Analytics in Smart Cities. Informatics 2017, 4, 16.
Kolomvatsos K, Anagnostopoulos C. Reinforcement Learning for Predictive Analytics in Smart Cities. Informatics. 2017; 4(3):16.Chicago/Turabian Style
Kolomvatsos, Kostas; Anagnostopoulos, Christos. 2017. "Reinforcement Learning for Predictive Analytics in Smart Cities." Informatics 4, no. 3: 16.
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