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FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
FedX - Optimization Techniques for Federated Query Processing on Linked Data
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FedX - Optimization Techniques for Federated Query Processing on Linked Data

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The final slides of our talk about FedX at the 10th International Semantic Web Conference in Bonn. For details about FedX see http://www.fluidops.com/fedx/

The final slides of our talk about FedX at the 10th International Semantic Web Conference in Bonn. For details about FedX see http://www.fluidops.com/fedx/

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  • Example scenarioDBpedia and New York Times collectionsDBpedia as structured knowledge baseNew York Times as a news provider
  • Minimize the number of remote requests due to intelligent algebraic query rewritings
  • Query Performance improved significantly for most queriesImprovements by more than an order of magnitudeNo timeouts, no evaluation errorsQuery CD3SELECT ?president ?party ?page WHERE { ?president <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/ontology/President> . ?president <http://dbpedia.org/ontology/nationality> <http://dbpedia.org/resource/United_States> . ?president <http://dbpedia.org/ontology/party> ?party . ?x <http://data.nytimes.com/elements/topicPage> ?page . ?x <http://www.w3.org/2002/07/owl#sameAs> ?president .}Query LS3SELECT ?Drug ?IntDrug ?IntEffect WHERE { ?Drug <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/ontology/Drug> . ?y <http://www.w3.org/2002/07/owl#sameAs> ?Drug . ?Int <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/interactionDrug1> ?y . ?Int <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/interactionDrug2> ?IntDrug . ?Int <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/text> ?IntEffect . }
  • Transcript

    • 1. FedX:Optimization Techniques for Federated Query Processing on Linked Data ISWC 2011 – October 26th Andreas Schwarte1, Peter Haase1, Katja Hose2, Ralf Schenkel2, and Michael Schmidt1 1fluidOperations AG, Walldorf 2Max-Planck Institute for Informatics, Saarbrücken
    • 2. Outline• Motivation• Federated Query Processing• FedX Query Processing Model• Optimization Techniques• Evaluation• Conclusion & Outlook 2
    • 3. Motivation Query processing involving multiple distributed data sources, e.g. Linked Open Data cloud New York Times DBpedia Query both data collections in an integrated way 3
    • 4. Federated Query Processing Federation mediator at the server Virtual integration of (remote) data sources Communication via SPARQL protocol SPARQL Data Source SPARQL Federation Query Data Source Mediator SPARQL Data Source 4
    • 5. Federated Query Processing Example Query Find US presidents and associated news articles SELECT ?President ?Party ?TopicPage WHERE { ?President rdf:type dbpedia-yago:PresidentsOfTheUnitedStates . ?nytPresident owl:sameAs ?President . ?President dbpedia:party ?Party . ?nytPresident nytimes:topicPage ?TopicPage . } 5
    • 6. Federated Query ProcessingQuery:SELECT ?President ?Party ?TopicPage WHERE { ?President rdf:type dbpedia-yago:PresidentsOfTheUnitedStates . ?nytPresident owl:sameAs ?President . SPARQL ...} DBpedia Federation “Barack Obama” Mediator “George W. Bush” ... ?President rdf:type dbpedia-yago:PresidentsOfTheUnitedStates . SPARQL “Barack Obama” “George W. Bush” NYTimes ... 6
    • 7. Federated Query Processing Query: SELECT ?President ?Party ?TopicPage WHERE { ?President rdf:type dbpedia-yago:PresidentsOfTheUnitedStates . ?nytPresident owl:sameAs ?President . SPARQL ... } DBpedia Federation yago:Obama Mediator ?nytPresident owl:sameAs “Barack Obama” . SPARQL “Barack Obama”Input: “George W. Bush” NYTimes ... “Barack Obama”, yago:Obama nyt:ObamaOutput: “Barack Obama”, nyt:Obama 7
    • 8. Federated Query Processing Query: SELECT ?President ?Party ?TopicPage WHERE { ?President rdf:type dbpedia-yago:PresidentsOfTheUnitedStates . SPARQL ... } DBpedia Federation Mediator ?nytPresident owl:sameAs “George W. Bush” . SPARQL “Barack Obama”Input: “George W. Bush” NYTimes ... “Barack Obama”, yago:Obama nyt:BushOutput: “Barack Obama”, nyt:Obama “George W. Bush”, nyt:Bush ... and so on for the other intermediate mappings and triple patterns ... 8
    • 9. FedX Query Processing ModelScenario: Efficient SPARQL query processing on multiple distributed sources Data sources are known and accessible as SPARQL endpoints • FedX is designed to be fully compatible with SPARQL 1.0 No a-priori knowledge about data sources • No local preprocessing of the data sources required • No need for pre-computed statistics On-demand federation setup 9
    • 10. Challenges to Federated Query Processing1) Involve only relevant sources in the evaluation Avoid: Subqueries are sent to all sources, although potentially irrelevant2) Compute joins close to the data Avoid: All joins are executed locally in a nested loop fashion3) Reduce remote communication Avoid: Nested loop join that causes many remote requests 10
    • 11. Optimization Techniques1. Source Selection:Idea: Triple patterns are annotated with relevant sources • Sources that can contribute information for a particular triple pattern • SPARQL ASK requests in conjunction with a local cache • After a warm-up period the cache learns the capabilities of the data sources  During Source Selection remote requests can be avoided2. Exclusive Groups:Idea: Group triple patterns with the same single relevant source • Evaluation in a single (remote) subquery • Push join to the endpoint 11
    • 12. Optimization Techniques (2)Example: Source Selection + Exclusive GroupsSELECT ?President ?Party ?TopicPage WHERE { Source Selection ?President rdf:type dbpedia-yago:PresidentsOfTheUnitedStates . @ DBpedia ?President dbpedia:party ?Party . @ DBpedia Exclusive Group ?nytPresident owl:sameAs ?President . @ DBpedia, NYTimes ?nytPresident nytimes:topicPage ?TopicPage . @ NYTimes} Advantages:  Avoid sending subqueries to sources that are not relevant  Delegate joins to the endpoint by forming exclusive groups (i.e. executing the respective patterns in a single subquery) 12
    • 13. Optimization Techniques (3)3. Join Order:Idea: Iteratively determine the join order based on count-heuristic: • Count free variables of triple patterns and groups • Consider "resolved" variable mappings from earlier iteration4. Bound Joins:Idea: Compute joins in a block nested loop fashion: • Reduce the number of requests by "vectored" evaluation of a set of input bindings • Renaming and Post-Processing technique for SPARQL 1.0 13
    • 14. Optimization Techniques (4)Example: Bound JoinsSELECT ?President ?Party ?TopicPage WHERE { ?President rdf:type dbpedia:PresidentsOfTheUnitedStates . ?President dbpedia:party ?Party . ?nytPresident owl:sameAs ?President . ?nytPresident nytimes:topicPage ?TopicPage .} Assume that the following intermediate results have been computed as input for the last triple pattern Block Input Before (NLJ) “Barack Obama” SELECT ?TopicPage WHERE { “Barack Obama” nytimes:topicPage ?TopicPage } “George W. Bush” SELECT ?TopicPage WHERE { “George W. Bush” nytimes:topicPage ?TopicPage } … … Now: Evaluation in a single remote request using a SPARQL UNION construct + local post processing (SPARQL 1.0) 14
    • 15. Optimization Example 1 SPARQL Query 2 Unoptimized Internal Representation Compute Micronutrients using Drugbank and KEGG 4x Local Join SELECT ?drug ?title WHERE { = ?drug drugbank:drugCategory drugbank-cat:micronutrient . ?drug drugbank:casRegistryNumber ?id . 4x NLJ ?keggDrug rdf:type kegg:Drug . ?keggDrug bio2rdf:xRef ?id . ?keggDrug dc:title ?title . }3 Optimized Internal Representation Exlusive Group  Remote Join 15
    • 16. EvaluationBased on FedBench benchmark suite 14 queries from the Cross Domain (CD) and Life Science (LS) collections Real-World Data from the Linked Open Data cloud Federation with 5 (CD) and 4 (LS) data sources Queries vary in complexity, size, structure, and sources involvedBenchmark environment HP Proliant 2GHz 4Core, 32GB RAM 20GB RAM for server (federation mediator) Local copies of the SPARQL endpoint to ensure reproducibility and reliability of the service • Provided by the FedBench Framework 16
    • 17. Evaluation (2) a) Evaluation times of Cross Domain (CD) and Life Science (LS) queries 17
    • 18. Evaluation (3) b) Number of requests sent to the endpoints Runtimes AliBaba: >600s DARQ: >600s FedX: 0.109s Runtimes AliBaba: >600s DARQ: 133s FedX: 1.4s 18
    • 19. FedX – The Bigger Picture Information Workbench: Integration of Virtualized Data Sources as a ServiceApplication Layer Semantic Wiki Collaboration Reporting & Analytics Visual ExplorationVirtualization Layer Transparent & On-Demand Integration of Data SourcesData Layer SPARQL SPARQL SPARQL Endpoint Endpoint Endpoint Data Registries Metadata CKAN, data.gov, etc. Registry Data Source Data Source + Enterprise Data Data Source 19
    • 20. Conclusion and OutlookFedX: Efficient SPARQL query processing in federated setting Available as open source software: http://www.fluidops.com/fedx • Implementation as a Sesame SAIL Novel join processing strategies, grouping techniques, source selection • Significant improvement of query performance compared to state-of-the-art systems On-demand federation setup without any preprocessing of data sources Compatible with existing SPARQL 1.0 endpointsOutlook SPARQL 1.1 Federation Extension in Sesame 2.6 • Ongoing integration of FedX’ optimization techniques into Sesame Federation Extensions in FedX • SERVICE keyword for optional user input to improve source selection • BINDINGS clauses for bound joins (for SPARQL 1.1 endpoints) (Remote) statistics to improve join order (e.g. VoID) 20
    • 21. Visit our exhibitor boothor arrange a private live demo!Further information on FedXhttp://www.fluidops.com/fedx THANK YOU CONTACT: fluid Operations Altrottstr. 31 Walldorf, Germany Email: info@fluidOps.com website: www.fluidOps.com Tel.: +49 6227 3846-527
    • 22. Query Cross Domain 3 (CD3) Find US presidents, their party and associated newsSELECT ?president ?party ?page WHERE { ?president <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/ontology/President> . ?president <http://dbpedia.org/ontology/nationality> <http://dbpedia.org/resource/United_States> . ?president <http://dbpedia.org/ontology/party> ?party . ?x <http://data.nytimes.com/elements/topicPage> ?page . ?x <http://www.w3.org/2002/07/owl#sameAs> ?president .}
    • 23. Query Life Science 3 (LS3) Find drugs and interaction drugs (+ the effect)SELECT ?Drug ?IntDrug ?IntEffect WHERE { ?Drug <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/ontology/Drug> . ?y <http://www.w3.org/2002/07/owl#sameAs> ?Drug . ?Int <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/interactionDrug1> ?y . ?Int <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/interactionDrug2> ?IntDrug . ?Int <http://www4.wiwiss.fu-berlin.de/drugbank/resource/drugbank/text> ?IntEffect .}

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