Eagle a Scalable Query Processing Engine for Linked Sensor Data
Abstract
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.
Highlights
The current standard query language for RDF, i.e., SPARQL 1.1, does not support spatio–temporal query patterns on sensor data. Recently, there have been several complimentary works towards supporting spatio–temporal queries on RDF. For example, to enable spatio–temporal analysis, in [19], Perry et al. propose the SPARQL-ST query language and introduce the formal syntax and semantics of their proposed language. SPARQL-ST is extended from the SPARQL language to support complex spatial and temporal queries on temporal RDF graphs containing spatial objects. With the same goal as SPARQL-ST, Koubarakis et al. propose st-SPARQL [20]. They introduce stRDF as a data model to model spatial and temporal information and the stSPARQL language to query against stRDF. Another example is [21], where Gutierrez et al. propose a framework that introduces temporal RDF graphs to support temporal reasoning on RDF data. In this approach, the temporal dimension is added to the RDF model. The temporal query language for temporal RDF graphs is also provided. However, the aforementioned works commonly focus on enabling spatio–temporal query features, but hardly any of them fully address the performance and scalability issues of querying billions of triples [22].
- OpenLink Virtuoso (https://github.com/openlink/virtuoso-opensource) utilize RDF query engines and spatial indices to manage spatial RDF data... Another example is OWLIM [27], which supports a geospatial index in its Standard Edition (SE). However, none of them systematically address the issue of elasticity and scalability for spatio–temporal analytic functions to deal with the massive volume of sensor data.
- these approaches only support limited spatial functions, and the spatial entities have to follow the GeoRSS GML
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