Benchmarking Bottom-Up and Top-Down Strategies to Sparql-To-Sql Query Translation

Authors

Andrii Kashliev, University of Texas-Pan American

Date of Award

8-2011

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Artem Chebotko

Second Advisor

Dr. Zhixiang Chen

Third Advisor

Dr. Pearl Brazier

Abstract

Many researchers have proposed using conventional relational databases to store and query large Semantic Web datasets. The most complex component of this approach is SPARQL-to-SQL query translation. Existing algorithms perform this translation using either bottom-up or top-down strategy and result in semantically equivalent but syntactically different relational queries. Do relational query optimizers always produce identical query execution plans for semantically equivalent bottom-up and top-down queries? Which of the two strategies yields faster SQL queries? To address these questions, this work studies bottom-up and top-down translations of SPARQL queries with nested optional graph patterns. This work presents: (1) A basic graph pattern translation algorithm that yields flat SQL queries, (2) A bottom-up nested optional graph pattern translation algorithm, (3) A top-down nested optional graph pattern translation algorithm, and (4) A performance study featuring SPARQL queries with nested optional graph patterns over RDF databases created in Oracle, DB2, and PostgreSQL.

Comments

Copyright 2011 Andrii Kashliev. All Rights Reserved.

https://www.proquest.com/dissertations-theses/benchmarking-bottom-up-top-down-strategies-sparql/docview/896958081/se-2?accountid=7119

Granting Institution

University of Texas-Pan American