Abstract
Due to the current lack of effectiveness on perception, management, and coordination for urban computing applications, a great number of semantic data has not yet been fully exploited and utilized, decreasing the effectiveness of urban services. To address the problem, we propose a semantic data management framework, RDFStore, for large-scale urban data management and query. RDFStore uses hashcode as the basic encoding pattern for semantic data storage. Based on the characteristics of strong connectedness of the data clique with different semantics, we construct indexes through the maximum clique on the whole semantic data. The large-scale semantic data of urban computing is organized and managed. On the basis of clique index, we adopt CLARANS clustering to enhance the accessibility of vertexes, and the data management is fulfilled. The experiment compares RDFStore to the mainstream platforms, and the results show that the proposed framework does enhance the effectiveness of semantic data management for urban computing applications.
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Song, S., Zhang, X., Guo, B. (2019). Large-Scale Semantic Data Management For Urban Computing Applications. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_8
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DOI: https://doi.org/10.1007/978-3-030-15093-8_8
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