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Smart cities are providing advanced services gathering data from different sources. Cities collect static data like road graphs, service description as well as dynamic/real time data like weather forecast, traffic sensors, bus positions, city sensors, events, emergency data, etc. RDF stores may be used to integrate all information coming from different sources and allow applications to use the data to provide new advanced services to the citizens and city administrators exploiting inferential capabilities and indexes. These city services are typically based on geographic positions and need to access quickly to the real time data (e.g., next time of bus arrival) as well as to the historical data to perform some data analysis to compute predictions (based on statistics and real time cumulated data). In this paper, the needs and constraints for RDF stores to be used for smart cities services and the currently available RDF stores are evaluated. The assessment model allows understanding if they are suitable as a basis for Smart City modeling and application.
The DISIT Smart City RDF Store benchmark proposed has been tuned for generic smart city services in order to compare results that can be obtained by using different RDF Stores. In the benchmark, particular emphasis is devoted to geo and full text searches that are partially considered in other well-known RDF store benchmarks as LUBM and BSBM. The paper reports the validation of the Smart City RDF Benchmark on the basis of Florence Smart City and Tuscany Region (Italy), comparing the results of adopting Virtuoso and GraphDB. Others graph database (RDF Stores) have been also initially taken into account but later not addressed since them do not cover the needed functionalities.
The benchmark includes (in progress):
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Any other information of RDF benchmarking would be appreciated.