| Title | On the application of Big Data in future large-scale intelligent Smart City installations |
|---|---|
| ID_Doc | 36227 |
| Authors | Girtelschmid, S; Steinbauer, M; Kumar, V; Fensel, A; Kotsis, G |
| Title | On the application of Big Data in future large-scale intelligent Smart City installations |
| Year | 2014 |
| Published | International Journal Of Pervasive Computing And Communications, 10, 2 |
| Abstract | Purpose - 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. Design/methodology/approach - 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. Findings - 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. Research limitations/implications - 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. Originality/value - 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. |
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