The ever-increasing amount of Internet of Things (IoT) data emanating from sensor and mobile devices is creating new capabilities and unprecedented economic opportunity for individuals, organizations and states. In comparison with traditional data sources, and in combination with other useful information sources, the data generated by sensors is also providing a meaningful spatio-temporal context. This spatio-temporal correlation feature turns the sensor data become even more valuables, especially for applications and services in Smart City, Smart Health-Care, Industry 4.0, etc. However, due to the heterogeneity and diversity of these data sources, their potential benefits will not be fully achieved if there are no suitable means to support interlinking and exchanging this kind of information. This challenge can be addressed by adopting the suite of technologies developed in the Semantic Web, such as Linked Data model and SPARQL. When using these technologies, and with respect to an application scenario which requires managing and querying a vast amount of sensor data, the task of selecting a suitable RDF engine that supports spatio-temporal RDF data is crucial. In this paper, we present our empirical studies of applying an RDF store for Linked Sensor Data. We propose an evaluation methodology and metrics that allow us to assess the readiness of an RDF store. An extensive performance comparison of the system-level aspects for a number of well-known RDF engines is also given. The results obtained can help to identify the gaps and shortcomings of current RDF stores and related technologies for managing sensor data which may be useful to others in their future implementation efforts.
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[v1] 2019-08-27 10:20:24
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