Query Execution for RDF Data on Row and Column Store

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• 作者：Trupti Padiya (18)
  Minal Bhise (18)
  Sandeep Vasani (18)
  Mohit Pandey (18)
  
• 关键词：Data Partitioning ; Data Scalability ; FOAF ; Materialized Views ; Query Execution ; Semantic Web
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8956
• 期：1
• 页码：403-408
• 全文大小：176 KB
• 参考文献：1. Abadi, D.J., Marcus, A., Madden, S.R., Hollenbach, K.: SW-Store: a vertically partitioned DBMS for Semantic Web data management. The VLDB Journal 鈥?The International Journal on Very Large Data Bases聽18(2), 385鈥?06 (2009) CrossRef
  2. Abadi, D.J., Madden, S.R., Hachem, N.: Column-stores vs. row-stores: how different are they really? In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 09-12 (2008)
  3. Sidirourgos, L., Goncalves, R., Kersten, M., Nes, N., Manegold, S.: Column-store support for RDF data management: not all swans are white. Proceedings of the VLDB Endowment聽1(2), 1553鈥?563 (2008) CrossRef
  4. FOAF Dataset (February 23, 2013), foaf-dataset-available/" class="a-plus-plus"> http://ebiquity.umbc.edu/blogger/2005/01/25/foaf-dataset-available/
  5. MonetDB Available: (March 1,2014), http://www.monetdb.org/Home
  6. Vasani, S., Pandey, M., Bhise, M., Padiya, T.: Faster Query Execution for Partitioned RDF Data. In: Hota, C., Srimani, P.K. (eds.) ICDCIT 2013. LNCS, vol.聽7753, pp. 547鈥?60. Springer, Heidelberg (2013) CrossRef
  
• 作者单位：Trupti Padiya (18)
  Minal Bhise (18)
  Sandeep Vasani (18)
  Mohit Pandey (18)
  
  18. DA-IICT Gandhinagar, India
  
• 丛书名：Distributed Computing and Internet Technology
• ISBN：978-3-319-14977-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

This paper shows experimental comparison between various data storage techniques to manage RDF data. The work represents evaluation of query performance in terms of query execution time and data scalability, using row and column store for various data storage techniques. To demonstrate these ideas FOAF (Friend Of A Friend) data is used. The paper contributes experimental and analytical study for application of partitioning techniques on FOAF data which makes queries 168 times faster compared to traditional triples table. Materialized views over vertically partitioned data show an additional 8 times improvement in query performance against partitioned data for the frequently occurring queries. Vertical partitioning is executed on column store also, and as FOAF data size scales, an order of magnitude improved performance is observed over row store execution.