As a web search engine is able to find relevant sources for a keyword query, the web of data clea... more As a web search engine is able to find relevant sources for a keyword query, the web of data clearly needs a source selection engine able to find relevant endpoints for a SPARQL query. However, source selection requires to get informations about the content of endpoints and currently, it remains difficult to automatically explore the content of endpoints as web robots explore the content of web servers. Thanks to the web preemption principle, we propose to automatically build RDF summaries of endpoints through SPARQL queries. We propose SemCat, an approach to compute the source selection of a query Q by evaluating a rewriting of Q on summaries. As all queries terminate under the web preemption paradigm, SemCat is able to provide a web automated source selection service relying on preemptable SPARQL servers. We empirically demonstrate that various summaries can be extracted with a data transfer proportional to the size of the summary, and highlight the trade-off between the size of t...
Executing aggregate queries on the web of data allows to compute useful statistics ranging from t... more Executing aggregate queries on the web of data allows to compute useful statistics ranging from the number of properties per class in a dataset to the average life of famous scientists per country. However, processing aggregate queries on public SPARQL endpoints is challenging, mainly due to quotas enforcement that prevents queries to deliver complete results. Existing distributed query engines allow to go beyond quota limitations, but their data transfer and execution times are clearly prohibitive when processing aggregate queries. Following the web preemption model, we define a new preemptable aggregation operator that allows to suspend and resume aggregate queries. Web preemption allows to continue query execution beyond quota limits and server-side aggregation drastically reduces data transfer and execution time of aggregate queries. Experimental results demonstrate that our approach outperforms existing approaches by orders of magnitude in terms of execution time and the amount...
Web integration systems are able to provide transparent and uniform access to heterogeneous Web d... more 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.
As a web search engine is able to find relevant sources for a keyword query, the web of data clea... more As a web search engine is able to find relevant sources for a keyword query, the web of data clearly needs a source selection engine able to find relevant endpoints for a SPARQL query. However, source selection requires to get informations about the content of endpoints and currently, it remains difficult to automatically explore the content of endpoints as web robots explore the content of web servers. Thanks to the web preemption principle, we propose to automatically build RDF summaries of endpoints through SPARQL queries. We propose SemCat, an approach to compute the source selection of a query Q by evaluating a rewriting of Q on summaries. As all queries terminate under the web preemption paradigm, SemCat is able to provide a web automated source selection service relying on preemptable SPARQL servers. We empirically demonstrate that various summaries can be extracted with a data transfer proportional to the size of the summary, and highlight the trade-off between the size of t...
Executing aggregate queries on the web of data allows to compute useful statistics ranging from t... more Executing aggregate queries on the web of data allows to compute useful statistics ranging from the number of properties per class in a dataset to the average life of famous scientists per country. However, processing aggregate queries on public SPARQL endpoints is challenging, mainly due to quotas enforcement that prevents queries to deliver complete results. Existing distributed query engines allow to go beyond quota limitations, but their data transfer and execution times are clearly prohibitive when processing aggregate queries. Following the web preemption model, we define a new preemptable aggregation operator that allows to suspend and resume aggregate queries. Web preemption allows to continue query execution beyond quota limits and server-side aggregation drastically reduces data transfer and execution time of aggregate queries. Experimental results demonstrate that our approach outperforms existing approaches by orders of magnitude in terms of execution time and the amount...
Web integration systems are able to provide transparent and uniform access to heterogeneous Web d... more 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.
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