To cope with the massive growth of semantic data streams, several RDF Stream Processing (RSP) engines have been implemented. The efficiency of their throughput, latency and memory consumption can be evaluated using available benchmarks such as LSBench and City- Bench. Nevertheless, these benchmarks lack an in-depth performance evaluation as some measurement metrics have not been considered. The main goal of this paper is to analyze the performance of two popular RSP engines, namely C-SPARQL and CQELS, when varying a set of performance metrics. More precisely, we evaluate the impact of stream rate, number of streams and window size on execution time as well as on memory consumption.
Despite the recent surge of research in query processing over data streams, little attention has been devoted to defining precise semantics for continuous queries over streams. We first present an abstract semantics based on several building blocks: formal definitions for streams and relations, mappings among them, and any relational query language. From these basics we define a precise interpretation for continuous queries over streams and relations. We then propose a concrete language, CQL (for Continuous Query Language), which instantiates the abstract semantics using SQL as the relational query language and window specifications derived from SQL-99 to map from streams to relations. We have implemented most of the CQL language in a Data Stream Management System at Stanford, and we have developed a public repository of data stream applications that includes a wide variety of queries expressed in CQL.