Efficient and Scalable SPARQL Query Processing with Transformed Table

Sheng Wei Huang, Chia Ho Yu, Ce-Kuen Shieh, Ming Fong Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Resource Description Framework (RDF) is the core technology of Semantic Web and has been more and more popular in recent years. With the rapid growth of the RDF data, the TripleStore, which is the query engine and RDF data storage, requires more scalable and efficient technologies. To improve the scalability and the performance of triple query, which is called SPARQL query processing, MapReduce programming model and NoSQL database system such as HBase are well-known solutions for large scale data processing. However, in general case, the subject of a triple is regarded as RowKey in the table. In some queries, finding matched triple patterns is a time-consuming job. Therefore, we design another table with different storage schema called Transformed Table to reduce the time cost for read operation. The experimental results show that using Transformed Table can improve the triple query performance significantly.

Original languageEnglish
Title of host publication2015 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages103-106
Number of pages4
ISBN (Electronic)9781479987603
DOIs
Publication statusPublished - 2015 Jun 11
Event2015 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2015 - New Orleans, United States
Duration: 2015 Mar 92015 Mar 12

Publication series

Name2015 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2015

Other

Other2015 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2015
Country/TerritoryUnited States
CityNew Orleans
Period15-03-0915-03-12

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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