The Odyssey Approach for Optimizing Federated SPARQL Queries

Gabriela Montoya, Hala Skaf-Molli, Katja Hose

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

25 Citations (Scopus)


Answering queries over a federation of SPARQL endpoints requires combining data from more than one data source. Optimizing queries in such scenarios is particularly challenging not only because of (i) the large variety of possible query execution plans that correctly answer the query but also because (ii) there is only limited access to statistics about schema and instance data of remote sources. To overcome these challenges, most federated query engines rely on heuristics to reduce the space of possible query execution plans or on dynamic programming strategies to produce optimal plans. Nevertheless, these plans may still exhibit a high number of intermediate results or high execution times because of heuristics and inaccurate cost estimations. In this paper, we present Odyssey, an approach that uses statistics that allow for a more accurate cost estimation for federated queries and therefore enables Odyssey to produce better query execution plans. Our experimental results show that Odyssey produces query execution plans that are better in terms of data transfer and execution time than state-of-the-art optimizers. Our experiments using the FedBench benchmark show execution time gains of at least 25 times on average.
Original languageEnglish
Title of host publicationThe Semantic Web - ISWC 2017 : 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part I
Publication date2017
ISBN (Print)978-3-319-68287-7
ISBN (Electronic)978-3-319-68288-4
Publication statusPublished - 2017
EventThe 16th International Semantic Web Conference - Vienna, Austria
Duration: 21 Oct 201731 Oct 2017
Conference number: 16th


ConferenceThe 16th International Semantic Web Conference
Internet address
SeriesLecture Notes in Computer Science


  • Federated Queries
  • Query Optimization
  • Join Ordering
  • Source Selection


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