際際滷

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Institute for Web Science and Technologies
                        University of Koblenz  Landau, Germany




SPLENDID: SPARQL Endpoint Federation
     Exploiting VOID Descriptions


          Olaf G旦rlitz, Steffen Staab
Motivation



    How to access a large number of linked data sources?




WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany   際際滷 2
Data Integration Approaches

           Data Warehouse                                  Link Traversal




   Efficient query execution                         Live Data Access
   Complete results                                  Flexible / On Demand
   Data copies                                       Incomplete results
   Inflexible                                        Biased by starting point

WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany        際際滷 3
Our Approach

                                Data Federation

                                                        Live data access
                                                        Flexible source integration
                                                        Effective query planning
                                                        Complete results


Hypothesis:
Efficient query federation is possible using core Semantic
Web technology (i.e. SPARQL endpoints, VoiD descriptions)


WeST Institute                   Olaf G旦rlitz
People and Knowledge Networks    COLD 2011, Bonn, Germany        際際滷 4
VoiD: Vocabulary of Interlinked Datasets




                                              }        General Information




                                              }        Basic statistics
                                                       triples = 732744



                                              }        Type statistics
                                                       chebi:Compound = 50477




                                              }        Predicate statistics
                                                       bio:formula = 39555




WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany             際際滷 5
Distributed Query Processing




Contribution:
Apply Best Practices of RDBMS for RDF Federation

                                                           http://code.google.com/p/rdffederator/
WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany           際際滷 6
Query Example



        Which drugs are categorized as micronutrients?




       SELECT油?drug油?title油WHERE油{
       油油?drug油drugbank:drugCategory油category:micronutrient油.
       油油?drug油drugbank:casRegistryNumber油?id油.
       油油?keggDrug油rdf:type油kegg:Drug油.
       油油?keggDrug油bio2rdf:xRef油?id油.
       油油?keggDrug油purl:title油?title油.油}
       }




WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany   際際滷 7
Query Processing


          Source Selection             Join Optimization   Query Execution




       SELECT油?drug油?title油WHERE油{
       油油?drug油drugbank:drugCategory油category:micronutrient油.
       油油?drug油drugbank:casRegistryNumber油?id油.
       油油?keggDrug油rdf:type油kegg:Drug油.
       油油?keggDrug油bio2rdf:xRef油?id油.
       油油?keggDrug油purl:title油?title油.油}
       }




WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany     際際滷 8
Query Processing


          Source Selection             Join Optimization       Query Execution



       1. Step: Index-based source mapping

       SELECT油?drug油?title油WHERE油{
       油油?drug油drugbank:drugCategory油category:micronutrient油.               drugbank
       油油?drug油drugbank:casRegistryNumber油?id油.                             drugbank
       油油?keggDrug油rdf:type油kegg:Drug油.                                     kegg
       油油?keggDrug油bio2rdf:xRef油?id油.                                       kegg
       油油?keggDrug油purl:title油?title油.油}                                    kegg, dbpedia, Chebi
       }

         predicate-index                                   type-index
         drugbank:drugCategory  drugbank                  kegg:Drug  kegg




WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany         際際滷 9
Query Processing


          Source Selection             Join Optimization   Query Execution



       2. Step: Refinement with ASK Queries

       SELECT油?drug油?title油WHERE油{
       油油?drug油drugbank:drugCategory油category:micronutrient油.
       油油?drug油drugbank:casRegistryNumber油?id油.
       油油?keggDrug油rdf:type油kegg:Drug油.
       油油?keggDrug油bio2rdf:xRef油?id油.
       油油?keggDrug油purl:title油?title油.油}
       }


        No index for subject / object values



WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany    際際滷 10
Query Processing


          Source Selection             Join Optimization   Query Execution



       3. Step: Grouping Triple Patterns

       SELECT油?drug油?title油WHERE油{
       油油?drug油drugbank:drugCategory油category:micronutrient油.
       油油?drug油drugbank:casRegistryNumber油?id油.                        } drugbank
       油油?keggDrug油rdf:type油kegg:Drug油.
       油油?keggDrug油bio2rdf:xRef油?id油.                                  } kegg
       油油?keggDrug油purl:title油?title油.油}                               } kegg, dbpedia, Chebi
       }


        + grouping sameAs patterns



WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany    際際滷 11
Join Order Optimization


          Source Selection             Join Optimization   Query Execution



    Dynamic Programming with statistics-based cost estimation

                                     bind join /
                                     hash join




WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany    際際滷 12
Evaluation


   FedBench Evaluation Suite                                  Measuring
     Life Science + Cross Domain Data                         #data sources selected
     different query characteristics                          query execution time


Orthogonal State-of-the-Art approaches:
                       DARQ                AliBaba            FedX              SPLENDID
 Statistics            ServiceDesc                                            VoiD
 Source                Statistics          All sources        ASK queries       Statistics +
 Selection             (predicates)                                             ASK queries
 Query                 DynProg             Heuristics         Heuristics        DynProg
 Optimization
 Query                 Bind join           Bind join          Bound Join +      Bind Join +
 Execution                                                    parallelization   Hash Join


WeST Institute                     Olaf G旦rlitz
People and Knowledge Networks      COLD 2011, Bonn, Germany          際際滷 13
Evaluation: Source Selection


          Source Selection                Join Optimization      Query Execution




                                owl:sameAs                    rdf:type


WeST Institute                     Olaf G旦rlitz
People and Knowledge Networks      COLD 2011, Bonn, Germany        際際滷 14
Evaluation: Query Optimization


          Source Selection             Join Optimization   Query Execution




WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany    際際滷 15
Conclusion



                           Publish more VoiD description!



                   VoiD-based query federation is efficient



What next?
 Combination with FedX
 Improving estimation and cost model
 Integrating SPARQL 1.1 features
WeST Institute                  Olaf G旦rlitz
People and Knowledge Networks   COLD 2011, Bonn, Germany   際際滷 16

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Splendid: SPARQL Endpoint Federation Exploiting VOID Descriptions

  • 1. Institute for Web Science and Technologies University of Koblenz Landau, Germany SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions Olaf G旦rlitz, Steffen Staab
  • 2. Motivation How to access a large number of linked data sources? WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 2
  • 3. Data Integration Approaches Data Warehouse Link Traversal Efficient query execution Live Data Access Complete results Flexible / On Demand Data copies Incomplete results Inflexible Biased by starting point WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 3
  • 4. Our Approach Data Federation Live data access Flexible source integration Effective query planning Complete results Hypothesis: Efficient query federation is possible using core Semantic Web technology (i.e. SPARQL endpoints, VoiD descriptions) WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 4
  • 5. VoiD: Vocabulary of Interlinked Datasets } General Information } Basic statistics triples = 732744 } Type statistics chebi:Compound = 50477 } Predicate statistics bio:formula = 39555 WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 5
  • 6. Distributed Query Processing Contribution: Apply Best Practices of RDBMS for RDF Federation http://code.google.com/p/rdffederator/ WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 6
  • 7. Query Example Which drugs are categorized as micronutrients? SELECT油?drug油?title油WHERE油{ 油油?drug油drugbank:drugCategory油category:micronutrient油. 油油?drug油drugbank:casRegistryNumber油?id油. 油油?keggDrug油rdf:type油kegg:Drug油. 油油?keggDrug油bio2rdf:xRef油?id油. 油油?keggDrug油purl:title油?title油.油} } WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 7
  • 8. Query Processing Source Selection Join Optimization Query Execution SELECT油?drug油?title油WHERE油{ 油油?drug油drugbank:drugCategory油category:micronutrient油. 油油?drug油drugbank:casRegistryNumber油?id油. 油油?keggDrug油rdf:type油kegg:Drug油. 油油?keggDrug油bio2rdf:xRef油?id油. 油油?keggDrug油purl:title油?title油.油} } WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 8
  • 9. Query Processing Source Selection Join Optimization Query Execution 1. Step: Index-based source mapping SELECT油?drug油?title油WHERE油{ 油油?drug油drugbank:drugCategory油category:micronutrient油. drugbank 油油?drug油drugbank:casRegistryNumber油?id油. drugbank 油油?keggDrug油rdf:type油kegg:Drug油. kegg 油油?keggDrug油bio2rdf:xRef油?id油. kegg 油油?keggDrug油purl:title油?title油.油} kegg, dbpedia, Chebi } predicate-index type-index drugbank:drugCategory drugbank kegg:Drug kegg WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 9
  • 10. Query Processing Source Selection Join Optimization Query Execution 2. Step: Refinement with ASK Queries SELECT油?drug油?title油WHERE油{ 油油?drug油drugbank:drugCategory油category:micronutrient油. 油油?drug油drugbank:casRegistryNumber油?id油. 油油?keggDrug油rdf:type油kegg:Drug油. 油油?keggDrug油bio2rdf:xRef油?id油. 油油?keggDrug油purl:title油?title油.油} } No index for subject / object values WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 10
  • 11. Query Processing Source Selection Join Optimization Query Execution 3. Step: Grouping Triple Patterns SELECT油?drug油?title油WHERE油{ 油油?drug油drugbank:drugCategory油category:micronutrient油. 油油?drug油drugbank:casRegistryNumber油?id油. } drugbank 油油?keggDrug油rdf:type油kegg:Drug油. 油油?keggDrug油bio2rdf:xRef油?id油. } kegg 油油?keggDrug油purl:title油?title油.油} } kegg, dbpedia, Chebi } + grouping sameAs patterns WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 11
  • 12. Join Order Optimization Source Selection Join Optimization Query Execution Dynamic Programming with statistics-based cost estimation bind join / hash join WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 12
  • 13. Evaluation FedBench Evaluation Suite Measuring Life Science + Cross Domain Data #data sources selected different query characteristics query execution time Orthogonal State-of-the-Art approaches: DARQ AliBaba FedX SPLENDID Statistics ServiceDesc VoiD Source Statistics All sources ASK queries Statistics + Selection (predicates) ASK queries Query DynProg Heuristics Heuristics DynProg Optimization Query Bind join Bind join Bound Join + Bind Join + Execution parallelization Hash Join WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 13
  • 14. Evaluation: Source Selection Source Selection Join Optimization Query Execution owl:sameAs rdf:type WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 14
  • 15. Evaluation: Query Optimization Source Selection Join Optimization Query Execution WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 15
  • 16. Conclusion Publish more VoiD description! VoiD-based query federation is efficient What next? Combination with FedX Improving estimation and cost model Integrating SPARQL 1.1 features WeST Institute Olaf G旦rlitz People and Knowledge Networks COLD 2011, Bonn, Germany 際際滷 16

Editor's Notes

  • #3: Pre-selected linked datasets Transparent query federation