ABSTRACT use different representations. For example, consider multiple sensor networks measuring the same type of physical phenomenon. Each sensor deployment may have its own way to represent semantically identical information, e.g., "wind speed" vs. "average wind speed," or "temperature" vs. "thermometer". If a user wants to obtain the latest wind speed or temperature data values over the region where all the sensor networks are deployed, the user must employ a mechanism for letting the system understand the semantically equivalent but different representations of data, in order to fully answer the query. One of the solutions to deal with heterogeneity is through the semantic annotation of sensor data (Sheth, Henson, & Sahoo, 2008) , and the provision of ontology-based access to it (Calbimonte, Corcho, & Gray, 2010; Taylor & Leidinger, 2011) . However, there is a lack of evidence of how this approach scales, especially with high data rates, and in push-based delivery of streaming data. In this article we focus on two problems in this context: (i) how to find relevant heterogeneous sensor data sources based on their metadata, and (ii) how to query streaming sensor data from these sources. We summarize our contributions as follows: • Our main contribution to the first problem is the use of the SSN ontology (Compton et al., in press), along with domain-specific vocabularies, for modeling sensor metadata and observations, augmented with mappings to the original sensor schemas. To this end, we use R2RML (Das, Sundara, & Cyganiak, 2012) (RDB-to-RDF mapping language) for mapping relational streams -instead of tables-to ontologies. Thus we use ontologies as a common model for representing sensor data and metadata, to make it possible to search for data sources and to access them through ontological schemas. • For the second problem, we propose a query rewriting and data translation approach that allows querying virtual RDF streams using the SPARQL language with streaming extensions. This approach exploits the R2RML mappings to provide access to the sensor streaming data, not only the metadata. Furthermore, we show that our query rewriting and execution mechanisms are applicable for both pull and push delivery modes, and also for various state-of-theart stream processing engines, such as SNEE (Galpin, Brenninkmeijer, Jabeen, . We provide empirical evidence of performance with respect to sampling rates and delivery latency in both pull and push-based modes. As an illustrative example, we show how the GSN implementation of this approach was used in a federated sensor network in the SwissExperiment (http://www.swiss-experiment.ch) project, a collaborative platform for sharing real-time sensor data across various institutions to improve environmental hazard forecasting and warning. The paper is organized as follows: first we introduce the fundamental concepts of ontology-based access to streams. We then discuss the modeling of sensor data, metadata and mappings. We also provide the theoretical foundations of query translation and how it can be implemented. An experimental evaluation is also presented, before our conclusions. Our approach is based on the fundamental concepts detailed below: streaming data querying, and semantic data access using ontology-based queries and mappings. A data stream consists of an unbounded sequence of values continuously appended, each of which carries a timestamp that typically indicates when it has been produced.
In REF , the authors use R2RML (RDB-to-RDF mapping language) for mapping streams stored in relational databases to ontological schemas.
Enabling Query Technologies for the Semantic Sensor Web
Int. J. Semantic Web Inf. Syst.
Int. J. Semantic Web Inf. Syst.
Computer Science