On the application of Big Data in future large‐scale intelligent Smart City installations. Issue 2 (2014)

• Record Type: Journal Article
• Authors: Sylva Girtelschmid; Matthias Steinbauer; Vikash Kumar; Anna Fensel; Gabriele Kotsis
• Is Part Of: International journal of pervasive computing and communications. Volume 10:Issue 2(2014)
• Page Start: 168
• Page End: 182
• Publication Date: 2014
• Subjects: Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-03-2014-0022
• Languages: English
• ISSNs: 1742-7371

Abstract

<abstract> <title> <x xml:space="preserve"> Abstract </x> </title> <p> <bold>Purpose</bold> – The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale. <bold>Design/methodology/approach</bold> – The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre‐processed by a Big Data streaming platform to lower the workload to be processed by the rule engine. <bold>Findings</bold> – By creating a real‐world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem. <bold>Research limitations/implications</bold> – In this article, real‐world sensor data from only two buildings were extrapolated for the simulations. Obviously, real‐world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work. <bold>Originality/value</bold> – The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum.</p> <ack> <title> <x xml:space="preserve"> Acknowledgements </x> </title> <p>This work is supported by the Austrian government and the City of Vienna within the competence center program COMET. It is part of the joint strategic project "Analysis on semantically structured data from smart buildings and smart grids" which is carried out by the Telecommunications Research Center Vienna (FTW) and the Institute of Telecooperation at the Johannes Kepler University (JKU), Linz.</p> </ack> </abstract>