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 ( 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.
Highlights
As we focus on a query streaming scenario, the Query Controller (QC) serves a huge number of queries and, the assignment process should be concluded in the minimum time; the lowest load L in order to minimize the load of the selected Query Processor (QP)
Smart public governance of Smart Cities should focus on the provision of novel ICT solutions to enhance the adopted technologies
We define the notion of a Query Controller (QC), a module that is responsible to handle the incoming queries and decide their assignment to a number of underlying Query Processors (QPs)
Summary
Smart public governance [1] has been proposed to emphasize the application of information and communication technologies in the public sector and to enhance every technological component realized in Smart Cities (SCs) [2]. Local authorities should change the way of management of the available infrastructures and be based on automated solutions that will facilitate novel services supporting the so-called vision and leadership [3] They should adopt technologies that will secure that efforts in SCs are coordinated rather than isolated. The bureaucratic model is based on government monitoring, citizens have less control over smart initiatives and have a more passive role [3] In such scenarios, there is the need for building efficient, automated mechanisms that will manage the available data, services and the interaction between citizens and local authorities. Analytics will give insights on the data and be the basis for supporting high level services offered to the citizens and public authorities
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