Reinforcement Learning for Predictive Analytics in Smart Cities
Journal Title: Informatics - Year 2017, Vol 4, Issue 3
Abstract
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 (QC) 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.
Authors and Affiliations
Kostas Kolomvatsos and Christos Anagnostopoulos
Keywords
Related Articles
- EP ID EP44101
- DOI https://doi.org/10.3390/informatics4030016
- Views 278
- Downloads 0
Embracing First-Person Perspectives in Soma-Based Design
A set of prominent designers embarked on a research journey to explore aesthetics in movement-based design. Here we unpack one of the design sensitivities unique to our practice: a strong first person perspective—where...
Internet of Tangible Things (IoTT): Challenges and Opportunities for Tangible Interaction with IoT
In the Internet of Things era, an increasing number of everyday objects are able to offer innovative services to the user. However, most of these devices provide only smartphone or web user interfaces. As a result, the...
Modifying Dialogical Strategy in Asynchronous Critical Discussions for Cross-Strait Chinese Learners
In this global era, critical thinking has become crucial for educators and learners. The purpose of this research was to explore how modifying a dialogical strategy in asynchronous online discussion forums impacted Chi...
Ambient Assisted Living for Improvement of Health and Quality of Life—A Special Issue of the Journal of Informatics
The demographic change with respect to the ageing of the population has been a worldwide trend. As a direct result, it has been recognised as causing substantial social transformation in the 21st century [1]. By 2050,...
Ubiquitous Learning Architecture to Enable Learning Path Design across the Cumulative Learning Continuum
The past twelve years have seen ubiquitous learning (u-learning) emerging as a new learning paradigm based on ubiquitous technology. By integrating a high level of mobility into the learning environment, u-learning ena...