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ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints


Maribel AcostaMaria-Esther Vidal, Tomas Lampo, Julio Castillo, Edna Ruckhaus



Published: 2011 November
Herausgeber: Lora Aroyo, Chris Welty, Harith Alani, Jamie Taylor, and Abraham Bernstein
Buchtitel: Proceedings of the 10th International Semantic Web Conference
Ausgabe: 1
Nummer: 1
Verlag: Springer-Verlag
Erscheinungsort: Berlin, Heidelberg
Organisation: 10th International Semantic Web Conference
Nicht-referierte Veröffentlichung
BibTeX

Kurzfassung
Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, fixed plans identified following the traditional optimize-thenexecute paradigm, may timeout as a consequence of endpoint availability. Second, because blocking operators are usually implemented, endpoint query engines are not able to incrementally produce results, and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedulers to data availability and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data traffic is bursty, and opportunistically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints, and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.

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