Theses and Dissertations - UTB/UTPA

Date of Award

5-2011

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Artem Chebotko

Second Advisor

Dr. John Abraham

Third Advisor

Dr. Pearl Brazier

Abstract

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.

Comments

Copyright 2011 Craig Franke. All Rights Reserved.

https://arxiv.org/abs/1105.2264

https://www.proquest.com/dissertations-theses/distributed-semantic-web-data-management-hbase/docview/875789963/se-2

Granting Institution

University of Texas-Pan American

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