Parallel Data Loading during Querying Deep Web and Linked Open Data with SPARQL

Web integration systems are able to provide transparent and uniform access to heterogeneous Web data sources by integrating views of Linked Data, Web Service results, or data extracted from the Deep Web. However, given the potential large number of views, query engines of Web integration systems have to implement execution techniques able to scale up to real-world scenarios and efficiently execute queries. We tackle the problem of SPARQL query processing against RDF views, and propose a non-blocking query execution strategy that incrementally accesses and merges the views relevant to a SPARQL query in a parallel fashion. The proposed strategy is implemented on top of Jena 2.7.4, and empirically compared with SemLAV, a sequential SPARQL query engine on RDF views. Results suggest that our approach outperforms SemLAV in terms of the number of answers produced per unit of time.