{"title":"Query Execution for RDF Data Using Structure Indexed Vertical Partitioning","authors":"Bhavik Shah, Trupti Padiya, Minal Bhise","doi":"10.1109/IPDPSW.2015.143","DOIUrl":null,"url":null,"abstract":"The paper explores use of various partitioning methods to store RDF data effectively, to meet the needs of extensively growing highly interactive semantic web applications. It proposes a combinational approach of structure index partitioning and vertical partitioning - SIVP and demonstrates the implementation of SIVP. The paper presents five metrics to measure and analyze performance of SIVP store. SIVP is experimented on FOAF and SwetoDBLP datasets. SIVP store have shown an average of 34% gain over vertical partitioning for FOAF dataset. For SwetoDBLP dataset, SIVP have shown an average of 26% gain over VP. SIVP is better than vertical partitioning provided extra time needed in SIVP, which consists of lookup time and merge time, is compensated by frequency of a query higher than breakeven point for that query.","PeriodicalId":340697,"journal":{"name":"2015 IEEE International Parallel and Distributed Processing Symposium Workshop","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Parallel and Distributed Processing Symposium Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPSW.2015.143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The paper explores use of various partitioning methods to store RDF data effectively, to meet the needs of extensively growing highly interactive semantic web applications. It proposes a combinational approach of structure index partitioning and vertical partitioning - SIVP and demonstrates the implementation of SIVP. The paper presents five metrics to measure and analyze performance of SIVP store. SIVP is experimented on FOAF and SwetoDBLP datasets. SIVP store have shown an average of 34% gain over vertical partitioning for FOAF dataset. For SwetoDBLP dataset, SIVP have shown an average of 26% gain over VP. SIVP is better than vertical partitioning provided extra time needed in SIVP, which consists of lookup time and merge time, is compensated by frequency of a query higher than breakeven point for that query.
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