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About this book
Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation, and Web technologies. As a consequence, the RDF data model is used in a variety of applications today for integrating knowledge and information: in open Web or government data via the Linked Open Data initiative, in scientific domains such as bioinformatics, and more recently in search engines and personal assistants of enterprises in the form of knowledge graphs.
Managing such large volumes of RDF data is challenging due to the sheer size, heterogeneity, and complexity brought by RDF reasoning. To tackle the size challenge, distributed architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications requiring distributed architectures for the scalability, fault tolerance, and elasticity features it provides. At the same time, interest in massively parallel processing has been renewed by the MapReduce model and many follow-up works, which aim at simplifying the deployment of massively parallel data management tasks in a cloud environment.
In this book, we study the state-of-the-art RDF data management in cloud environments and parallel/distributed architectures that were not necessarily intended for the cloud, but can easily be deployed therein. After providing a comprehensive background on RDF and cloud technologies, we explore four aspects that are vital in an RDF data management system: data storage, query processing, query optimization, and reasoning. We conclude the book with a discussion on open problems and future directions.
Table of contents (7 chapters)
Authors and Affiliations
About the authors
Stamatis Zampetakis is an R&D engineer at TIBCO Orchestra Networks and a PMC member of Apache Calcite. Previously, he was a postdoctoral researcher at Inria, from where he also received his Ph.D. in 2015. Before that, he worked in FORTH-ICS as a research assistant. His research interests are in the broad area of query optimization with emphasis on RDF query processing and visualization.
Bibliographic Information
Book Title: Cloud-Based RDF Data Management
Authors: Zoi Kaoudi, Ioana Manolescu, Stamatis Zampetakis
Series Title: Synthesis Lectures on Data Management
DOI: https://doi.org/10.1007/978-3-031-01875-6
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 9
Copyright Information: Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-031-00747-7Published: 24 February 2020
eBook ISBN: 978-3-031-01875-6Published: 31 May 2022
Series ISSN: 2153-5418
Series E-ISSN: 2153-5426
Edition Number: 1
Number of Pages: XII, 91
Topics: Information Systems and Communication Service, Data Structures and Information Theory