Title: Hybrid data model of PACE and quadruple: an efficient data model for cloud computing

Authors: A. Clara Kanmani; V. Suma; N. Guruprasad

Addresses: Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi, India ' Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi, India ' Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi, India

Abstract: Cloud computing is a promising computing paradigm that involves outsourcing of computing resources with the capabilities of on demand provisioning with little or no up-front investment costs. Resource description framework (RDF) is the semantic data model for cloud computing which provides interoperability but is not effective in terms of scalability, formal semantics and query optimisation and reification. One of the key challenges in cloud computing therefore is to enhance RDF data model. This paper introduces a data model which uses hybrid approach of provenance aware context entity (PACE) and quadruples method of reification. The scope of the research is to understand the importance of RDF reification, limitations and its impact on creating an efficient data model. This hybrid RDF data model is deployed and tested for its performance on the AWS public cloud. Experimental results indicate that the proposed hybrid data model enhances accessibility, maintainability, and also accelerates query execution time.

Keywords: cloud computing; semantic web; resource description framework; RDF; data model; provenance aware context entity; PACE; quadruple.

DOI: 10.1504/IJCAET.2020.108108

International Journal of Computer Aided Engineering and Technology, 2020 Vol.13 No.1/2, pp.73 - 100

Received: 09 Sep 2017
Accepted: 27 Nov 2017
Published online: 03 Jul 2020 *

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