Efficient source selection and benchmarking for SPARQL Endpoint Query Federation /:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Körperschaft: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam, Netherlands :
IOS Press,
2018.
|
| Schriftenreihe: | Studies on the Semantic Web ;
vol. 026. |
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781614998402 161499840X |
Internformat
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| 100 | 1 | |a Saleem, Muhammad, |e author. | |
| 245 | 1 | 0 | |a Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / |c Muhammad Saleem. |
| 264 | 1 | |a Amsterdam, Netherlands : |b IOS Press, |c 2018. | |
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| 490 | 1 | |a Studies on the Semantic Web ; |v vol. 026 | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed September 27, 2018). | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Intro; Title Page; Acknowledgments; Contents; Abstract; Introduction; Federated SPARQL Query Processing; The Need for Efficient Source Selection; The Need for More Comprehensive SPARQL Benchmarks; Contributions; Chapter Overview; Basic Concepts and Notation; Semantic Web; URIs, RDF; SPARQL Query Language; Triplestore; SPARQL Syntax, Semantic and Notation; State of the Art; Federation systems evaluations; Benchmarks; Federated engines public survey; Survey Design; Discussion of the survey results; Details of selected systems; Overview of the selected approaches; Performance Variables. | |
| 505 | 8 | |a EvaluationExperimental setup; Evaluation criteria; Experimental results; Discussion; Effect of the source selection time; Effect of the data partitioning; Hypergraph-Based Source Selection; Problem Statement; HiBISCuS; Queries as Directed Labelled Hypergraphs; Data Summaries; Source Selection Algorithm; Pruning approach; Evaluation; Experimental Setup; Experimental Results; Trie-based Source Selection; TBSS; TBSS Data Summaries; TBSS Source Selection Algorithm; TBSS Pruning approach; QUETSAL; Quetsal's Architecture; Quetsal's SPARQL 1.1 Query Re-writing; Evaluation; Experimental Setup. | |
| 505 | 8 | |a Experimental ResultsDuplicate-Aware Source Selection; DAW; Min-Wise Independent Permutations (MIPs); DAW Index; DAW Federated Query Processing; Experimental Evaluation; Experimental Setup; Experimental Results; Policy-Aware Source Selection; Motivating Scenario; Methodology and Architecture; Evaluation; Experimental Setup; Experimental Results; Data Distribution-Based Source Selection; Motivation; Biological query example; Methods; Transforming TCGA data to RDF; Linking TCGA to the LOD cloud; TCGA data workflow and schema; Data distribution and load balancing. | |
| 505 | 8 | |a TopFed federated query processing approachSource selection; Results and discussion; Evaluation; Availability of supporting data; LargeRDFBench: SPARQL Federation Benchmark; Background; The Need of More Comprehensive SPARQL Federation Benchmark; Benchmark Description; Benchmark Datasets; Benchmark Queries; Performance Metrics; Evaluation; Experimental Setup; SPARQL 1.0 Experimental Results; SPARQL 1.1 Experimental Results; FEASIBLE: SPARQL Benchmarks Generation Framework; Key SPARQL Features; A Comparison of Existing Triple Stores Benchmarks and Query Logs; FEASIBLE Benchmark Generation. | |
| 505 | 8 | |a Data Set CleaningNormalization of Features Vectors; Query Selection; Complexity Analysis; Evaluation and Results; Composite Error Estimation; Experimental Setup; Experimental Results; Conclusion; HiBISCuS; TBSS/Quetsal; DAW; SAFE; TopFed; LargeRDFBench; FEASIBLE; Bibliography. | |
| 650 | 0 | |a SPARQL (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2014100282 | |
| 650 | 0 | |a Querying (Computer science) |0 http://id.loc.gov/authorities/subjects/sh2005008252 | |
| 650 | 0 | |a Semantic computing. |0 http://id.loc.gov/authorities/subjects/sh2009007899 | |
| 650 | 0 | |a Database searching. |0 http://id.loc.gov/authorities/subjects/sh86007858 | |
| 650 | 6 | |a SPARQL (Langage de programmation) | |
| 650 | 6 | |a Bases de données |x Interrogation. | |
| 650 | 6 | |a Informatique sémantique. | |
| 650 | 7 | |a online searching. |2 aat | |
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| contents | Intro; Title Page; Acknowledgments; Contents; Abstract; Introduction; Federated SPARQL Query Processing; The Need for Efficient Source Selection; The Need for More Comprehensive SPARQL Benchmarks; Contributions; Chapter Overview; Basic Concepts and Notation; Semantic Web; URIs, RDF; SPARQL Query Language; Triplestore; SPARQL Syntax, Semantic and Notation; State of the Art; Federation systems evaluations; Benchmarks; Federated engines public survey; Survey Design; Discussion of the survey results; Details of selected systems; Overview of the selected approaches; Performance Variables. EvaluationExperimental setup; Evaluation criteria; Experimental results; Discussion; Effect of the source selection time; Effect of the data partitioning; Hypergraph-Based Source Selection; Problem Statement; HiBISCuS; Queries as Directed Labelled Hypergraphs; Data Summaries; Source Selection Algorithm; Pruning approach; Evaluation; Experimental Setup; Experimental Results; Trie-based Source Selection; TBSS; TBSS Data Summaries; TBSS Source Selection Algorithm; TBSS Pruning approach; QUETSAL; Quetsal's Architecture; Quetsal's SPARQL 1.1 Query Re-writing; Evaluation; Experimental Setup. Experimental ResultsDuplicate-Aware Source Selection; DAW; Min-Wise Independent Permutations (MIPs); DAW Index; DAW Federated Query Processing; Experimental Evaluation; Experimental Setup; Experimental Results; Policy-Aware Source Selection; Motivating Scenario; Methodology and Architecture; Evaluation; Experimental Setup; Experimental Results; Data Distribution-Based Source Selection; Motivation; Biological query example; Methods; Transforming TCGA data to RDF; Linking TCGA to the LOD cloud; TCGA data workflow and schema; Data distribution and load balancing. TopFed federated query processing approachSource selection; Results and discussion; Evaluation; Availability of supporting data; LargeRDFBench: SPARQL Federation Benchmark; Background; The Need of More Comprehensive SPARQL Federation Benchmark; Benchmark Description; Benchmark Datasets; Benchmark Queries; Performance Metrics; Evaluation; Experimental Setup; SPARQL 1.0 Experimental Results; SPARQL 1.1 Experimental Results; FEASIBLE: SPARQL Benchmarks Generation Framework; Key SPARQL Features; A Comparison of Existing Triple Stores Benchmarks and Query Logs; FEASIBLE Benchmark Generation. Data Set CleaningNormalization of Features Vectors; Query Selection; Complexity Analysis; Evaluation and Results; Composite Error Estimation; Experimental Setup; Experimental Results; Conclusion; HiBISCuS; TBSS/Quetsal; DAW; SAFE; TopFed; LargeRDFBench; FEASIBLE; Bibliography. |
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| indexdate | 2025-04-11T08:46:29Z |
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| series | Studies on the Semantic Web ; |
| series2 | Studies on the Semantic Web ; |
| spelling | Saleem, Muhammad, author. Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / Muhammad Saleem. Amsterdam, Netherlands : IOS Press, 2018. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Studies on the Semantic Web ; vol. 026 Online resource; title from PDF title page (EBSCO, viewed September 27, 2018). Includes bibliographical references. Intro; Title Page; Acknowledgments; Contents; Abstract; Introduction; Federated SPARQL Query Processing; The Need for Efficient Source Selection; The Need for More Comprehensive SPARQL Benchmarks; Contributions; Chapter Overview; Basic Concepts and Notation; Semantic Web; URIs, RDF; SPARQL Query Language; Triplestore; SPARQL Syntax, Semantic and Notation; State of the Art; Federation systems evaluations; Benchmarks; Federated engines public survey; Survey Design; Discussion of the survey results; Details of selected systems; Overview of the selected approaches; Performance Variables. EvaluationExperimental setup; Evaluation criteria; Experimental results; Discussion; Effect of the source selection time; Effect of the data partitioning; Hypergraph-Based Source Selection; Problem Statement; HiBISCuS; Queries as Directed Labelled Hypergraphs; Data Summaries; Source Selection Algorithm; Pruning approach; Evaluation; Experimental Setup; Experimental Results; Trie-based Source Selection; TBSS; TBSS Data Summaries; TBSS Source Selection Algorithm; TBSS Pruning approach; QUETSAL; Quetsal's Architecture; Quetsal's SPARQL 1.1 Query Re-writing; Evaluation; Experimental Setup. Experimental ResultsDuplicate-Aware Source Selection; DAW; Min-Wise Independent Permutations (MIPs); DAW Index; DAW Federated Query Processing; Experimental Evaluation; Experimental Setup; Experimental Results; Policy-Aware Source Selection; Motivating Scenario; Methodology and Architecture; Evaluation; Experimental Setup; Experimental Results; Data Distribution-Based Source Selection; Motivation; Biological query example; Methods; Transforming TCGA data to RDF; Linking TCGA to the LOD cloud; TCGA data workflow and schema; Data distribution and load balancing. TopFed federated query processing approachSource selection; Results and discussion; Evaluation; Availability of supporting data; LargeRDFBench: SPARQL Federation Benchmark; Background; The Need of More Comprehensive SPARQL Federation Benchmark; Benchmark Description; Benchmark Datasets; Benchmark Queries; Performance Metrics; Evaluation; Experimental Setup; SPARQL 1.0 Experimental Results; SPARQL 1.1 Experimental Results; FEASIBLE: SPARQL Benchmarks Generation Framework; Key SPARQL Features; A Comparison of Existing Triple Stores Benchmarks and Query Logs; FEASIBLE Benchmark Generation. Data Set CleaningNormalization of Features Vectors; Query Selection; Complexity Analysis; Evaluation and Results; Composite Error Estimation; Experimental Setup; Experimental Results; Conclusion; HiBISCuS; TBSS/Quetsal; DAW; SAFE; TopFed; LargeRDFBench; FEASIBLE; Bibliography. SPARQL (Computer program language) http://id.loc.gov/authorities/subjects/sh2014100282 Querying (Computer science) http://id.loc.gov/authorities/subjects/sh2005008252 Semantic computing. http://id.loc.gov/authorities/subjects/sh2009007899 Database searching. http://id.loc.gov/authorities/subjects/sh86007858 SPARQL (Langage de programmation) Bases de données Interrogation. Informatique sémantique. online searching. aat COMPUTERS Intelligence (AI) & Semantics. bisacsh Database searching fast Querying (Computer science) fast Semantic computing fast SPARQL (Computer program language) fast IOS Press. https://id.oclc.org/worldcat/entity/E39QH7Jmq4CDTK3QFTckWWjytg http://id.loc.gov/authorities/names/no2015091156 has work: Efficient source selection and benchmarking for SPARQL endpoint query federation (Text) https://id.oclc.org/worldcat/entity/E39PCFDWfmqVxtVYJJ9GmrcCcd https://id.oclc.org/worldcat/ontology/hasWork Studies on the Semantic Web ; vol. 026. http://id.loc.gov/authorities/names/no2009156151 |
| spellingShingle | Saleem, Muhammad Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / Studies on the Semantic Web ; Intro; Title Page; Acknowledgments; Contents; Abstract; Introduction; Federated SPARQL Query Processing; The Need for Efficient Source Selection; The Need for More Comprehensive SPARQL Benchmarks; Contributions; Chapter Overview; Basic Concepts and Notation; Semantic Web; URIs, RDF; SPARQL Query Language; Triplestore; SPARQL Syntax, Semantic and Notation; State of the Art; Federation systems evaluations; Benchmarks; Federated engines public survey; Survey Design; Discussion of the survey results; Details of selected systems; Overview of the selected approaches; Performance Variables. EvaluationExperimental setup; Evaluation criteria; Experimental results; Discussion; Effect of the source selection time; Effect of the data partitioning; Hypergraph-Based Source Selection; Problem Statement; HiBISCuS; Queries as Directed Labelled Hypergraphs; Data Summaries; Source Selection Algorithm; Pruning approach; Evaluation; Experimental Setup; Experimental Results; Trie-based Source Selection; TBSS; TBSS Data Summaries; TBSS Source Selection Algorithm; TBSS Pruning approach; QUETSAL; Quetsal's Architecture; Quetsal's SPARQL 1.1 Query Re-writing; Evaluation; Experimental Setup. Experimental ResultsDuplicate-Aware Source Selection; DAW; Min-Wise Independent Permutations (MIPs); DAW Index; DAW Federated Query Processing; Experimental Evaluation; Experimental Setup; Experimental Results; Policy-Aware Source Selection; Motivating Scenario; Methodology and Architecture; Evaluation; Experimental Setup; Experimental Results; Data Distribution-Based Source Selection; Motivation; Biological query example; Methods; Transforming TCGA data to RDF; Linking TCGA to the LOD cloud; TCGA data workflow and schema; Data distribution and load balancing. TopFed federated query processing approachSource selection; Results and discussion; Evaluation; Availability of supporting data; LargeRDFBench: SPARQL Federation Benchmark; Background; The Need of More Comprehensive SPARQL Federation Benchmark; Benchmark Description; Benchmark Datasets; Benchmark Queries; Performance Metrics; Evaluation; Experimental Setup; SPARQL 1.0 Experimental Results; SPARQL 1.1 Experimental Results; FEASIBLE: SPARQL Benchmarks Generation Framework; Key SPARQL Features; A Comparison of Existing Triple Stores Benchmarks and Query Logs; FEASIBLE Benchmark Generation. Data Set CleaningNormalization of Features Vectors; Query Selection; Complexity Analysis; Evaluation and Results; Composite Error Estimation; Experimental Setup; Experimental Results; Conclusion; HiBISCuS; TBSS/Quetsal; DAW; SAFE; TopFed; LargeRDFBench; FEASIBLE; Bibliography. SPARQL (Computer program language) http://id.loc.gov/authorities/subjects/sh2014100282 Querying (Computer science) http://id.loc.gov/authorities/subjects/sh2005008252 Semantic computing. http://id.loc.gov/authorities/subjects/sh2009007899 Database searching. http://id.loc.gov/authorities/subjects/sh86007858 SPARQL (Langage de programmation) Bases de données Interrogation. Informatique sémantique. online searching. aat COMPUTERS Intelligence (AI) & Semantics. bisacsh Database searching fast Querying (Computer science) fast Semantic computing fast SPARQL (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2014100282 http://id.loc.gov/authorities/subjects/sh2005008252 http://id.loc.gov/authorities/subjects/sh2009007899 http://id.loc.gov/authorities/subjects/sh86007858 |
| title | Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / |
| title_auth | Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / |
| title_exact_search | Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / |
| title_full | Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / Muhammad Saleem. |
| title_fullStr | Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / Muhammad Saleem. |
| title_full_unstemmed | Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / Muhammad Saleem. |
| title_short | Efficient source selection and benchmarking for SPARQL Endpoint Query Federation / |
| title_sort | efficient source selection and benchmarking for sparql endpoint query federation |
| topic | SPARQL (Computer program language) http://id.loc.gov/authorities/subjects/sh2014100282 Querying (Computer science) http://id.loc.gov/authorities/subjects/sh2005008252 Semantic computing. http://id.loc.gov/authorities/subjects/sh2009007899 Database searching. http://id.loc.gov/authorities/subjects/sh86007858 SPARQL (Langage de programmation) Bases de données Interrogation. Informatique sémantique. online searching. aat COMPUTERS Intelligence (AI) & Semantics. bisacsh Database searching fast Querying (Computer science) fast Semantic computing fast SPARQL (Computer program language) fast |
| topic_facet | SPARQL (Computer program language) Querying (Computer science) Semantic computing. Database searching. SPARQL (Langage de programmation) Bases de données Interrogation. Informatique sémantique. online searching. COMPUTERS Intelligence (AI) & Semantics. Database searching Semantic computing |
| work_keys_str_mv | AT saleemmuhammad efficientsourceselectionandbenchmarkingforsparqlendpointqueryfederation AT iospress efficientsourceselectionandbenchmarkingforsparqlendpointqueryfederation |