| Abstract |
Smart cities are providing advanced services aggregating and exploiting data from different sources. Cities collect static data such as road graphs, service description, as well as dynamic/real time data like weather forecast, traffic sensors, bus positions, city sensors, events, emergency data, flows, etc. RDF stores may be used to set up knowledge bases integrating heterogeneous information for web and mobile applications to use the data for new advanced services to citizens and city administrators, thus exploiting inferential capabilities, temporal and spatial reasoning, and text indexing. In this paper, the needs and constraints for RDF stores to be used for smart cities services, together with the currently available RDF stores are evaluated. The assessment model allows a full understanding of whether an RDF store is suitable to be used as a basis for Smart City modeling and applications. The RDF assessment model is also supported by a benchmark which extends available RDF store benchmarks at the state the art. The comparison of the RDF stores has been applied on a number of well-known RDF stores as Virtuoso, GraphDB (former OWLIM), Oracle, StarDog, and many others. The paper also reports the adoption of the proposed Smart City RDF Benchmark on the basis of Florence Smart City model, data sets and tools accessible as Km4City Http://www.Km4City.org, and adopted in the European Commission international smart city projects named RESOLUTE H2020, REPLICATE H2020, and in Sii-Mobility National Smart City project in Italy. (C) 2018 The Authors. Published by Elsevier Ltd. |