Handling RDF Streams: Harmonizing Subgraph Matching, Adaptive Incremental Maintenance, and Matching-free Updates Together

Abstract

RDF stream processing (RSP) has become a vibrant area of research in the Semantic Web community, which guarantees interoperability and opens up important applications. There have been efforts to extend RDF data and SPARQL query for representing streaming information and continuous querying functionalities. However, existing solutions will incur significant low throughput due to the recomputation of the results from scratch as the window slides. In this paper, we propose a novel graph-based framework, referred as IncTreeRDF, towards continuous SPARQL query evaluation over RDF data streams. Under the framework, the RDF data streams are modeled as streaming graphs; the SPARQL queries are translated into graph patterns and evaluated via continuous sub-graph pattern-matching over streaming RDF graphs. IncTreeRDF employs a query-centric auxiliary data structure called TStore to store some intermediate results, which supports fast incremental maintenance. Based on TStore, we can not only avoid re-computing matches of the query but also prune invalid updates. Besides, we define matching-free update, in which subgraph matching calculation can be avoided under this scenario. Extensive experimental results show that IncTreeRDF significantly outperforms existing competitors.