Storage and Query Processing Architectures for RDF Data

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Author(s): Tanvi Chawla (Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, India)
Copyright: 2023
Pages: 16
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch019

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

The escalation in demand of RDF format for knowledge representation and information management can be attributed to its flexible nature. The RDF data model is increasingly being used for sharing and integration of information and knowledge across several domains. Some of the domains and applications where RDF model is increasingly being used include bioinformatics and search engines. As the amount of RDF data continues to increase, the management of such large amount of data becomes challenging. Thus, scalability is a major concern while handling large-scale RDF data. Hence, it becomes necessary to opt for scalable solutions while managing large RDF data. As a solution to this, many researchers opt for distributed data management systems. In this article, the authors provide a detailed analysis of RDF data management techniques used to make a RDF system more scalable. The objective of this article is to provide a brief description of the centralized and distributed RDF frameworks.

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Storage and Query Processing Architectures for RDF Data

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