Cloud-based RDF data management / by Zoi Kaoudi [and three others].

By:

Kaoudi, Zoi [author]

Contributor(s):

Material type:

Computer fileLanguage: English Publication details: San Rafael, California : Morgan & Claypool, 2020Description: 1 online resource (105, pages) : color illustrationsContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781681730349 (e-book)

Subject(s):

LOC classification:

QA76.76 K14 2020

Online resources:

Contents:

1. Introduction -- 2. Preliminaries -- 3. Cloud-based RDF storage -- 4. Cloud-based SPARQL query processing -- 5. SPARQL query optimization for the cloud -- 6. RDFS reasoning in the cloud -- 7. Concluding remarks

Summary: 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.

List(s) this item appears in: Online E-Books 2022

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Holdings
Item type	Current library	Collection	Call number	Materials specified	Status	Notes	Date due	Barcode
Online E-Books	Ladislao N. Diwa Memorial Library	Non-fiction	OEBP QA76.76 K14 2020 (Browse shelf(Opens below))		Available	OEBP000169		OEBP000169
Compact Discs	Ladislao N. Diwa Memorial Library Multimedia Section	Non-fiction	EB QA76.76 K14 2020 (Browse shelf(Opens below))		Room use only	CD0000922		CD0000922

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Include bibliographical references and index

1. Introduction -- 2. Preliminaries -- 3. Cloud-based RDF storage -- 4. Cloud-based SPARQL query processing -- 5. SPARQL query optimization for the cloud --
6. RDFS reasoning in the cloud -- 7. Concluding remarks

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.

Fund 164 CE-Logic, Inc. Purchased March 2, 2021 OEBP000169 P. Roderno PHP 12,778.00
2021-03-110 7813 to 7820

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