Emrooz: A scalable database for SSN observations

The design of ontologies for sensor data and metadata has received considerable attention. The most prominent is arguably the Semantic Sensor Network (SSN) ontology. For persistence and retrieval of sensor observations, systems that adopt the SSN ontology most obviously build on an RDF database (triple store). However, large volumes of collected sensor data can be challenging for RDF databases, as the evaluation of SPARQL queries for SSN observations quickly becomes prohibitively expensive. This is arguably due to the fact that triple stores are optimized to efficiently evaluate graph pattern queries, not time series interval queries. As our main contribution, we present Emrooz, a scalable database capable of consuming SSN observations represented in RDF and evaluating queries for SSN observations formulated in SPARQL. We present the Emrooz implementation on Apache Cassandra and Sesame and its performance compared to two state-of-the-art RDF databases. The results show that Emrooz query performance outperforms the two RDF databases by orders of magnitude with increasingly large datasets. We motivate the need for scalable databases for SSN observations on a case study in micrometeorology.