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Informatics 2017, 4(3), 16;

Reinforcement Learning for Predictive Analytics in Smart Cities

Department of Computer Science, University of Thessaly, Papasiopoulou 2-4, Lamia 35100, Greece;
School of Computing Science, University of Glasgow, 17 Lilybank Gardens, Glasgow G12 8QQ, UK
Author to whom correspondence should be addressed.
Academic Editor: Manuel Pedro Rodríguez Bolívar
Received: 30 March 2017 / Revised: 26 May 2017 / Accepted: 22 June 2017 / Published: 24 June 2017
(This article belongs to the Special Issue Smart Government in Smart Cities)
Full-Text   |   PDF [1273 KB, uploaded 29 June 2017]   |  


The 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 ( Q C ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework. View Full-Text
Keywords: reinforcement learning; Q-learning; clustering; data fusion; big data analytics; query streams reinforcement learning; Q-learning; clustering; data fusion; big data analytics; query streams

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Kolomvatsos, K.; Anagnostopoulos, C. Reinforcement Learning for Predictive Analytics in Smart Cities. Informatics 2017, 4, 16.

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