Cost- and Robustness-Based Query Optimization for Linked Data Fragments
Cost- and Robustness-Based Query Optimization for Linked Data Fragments
Cost- and Robustness-Based Query Optimization for Linked Data Fragments
Published: 2020
November
Buchtitel: The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference
Ausgabe: 12506
Reihe: Lecture Notes in Computer Science
Verlag: Springer
Nicht-referierte Veröffentlichung
BibTeX
Kurzfassung
Client-side SPARQL query processing enables evaluating queries over RDF datasets published on the Web without producing high loads on the data providers’ servers. Triple Pattern Fragment (TPF) servers provide means to publish highly available RDF data on the Web and clients to evaluate SPARQL queries over them have been proposed. For clients to devise efficient query plans that minimize both the number of requests submitted to the server as well as the overall execution time, it is key to accurately estimate join cardinalities to appropriately place physical join operators. However, collecting accurate and fine-grained statistics from remote sources is a challenging task, and clients typically rely on the metadata provided by the TPF server. Addressing this shortcoming, we propose CROP, a cost- and robust-based query optimizer to devise efficient plans combining both cost and robustness of query plans. The idea of robustness is determining the impact of join cardinality estimation errors on the cost of a query plan and to avoid plans where this impact is very high. In our experimental study, we show that our concept of robustness complements the cost model and improves the efficiency of query plans. Additionally, we show that our approach outperforms existing TPF clients in terms of overall runtime and number of requests.
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DOI Link: 10.1007/978-3-030-62419-4\_14