國立虎尾科技大學 |

Cloud-based RDF data management /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Cloud-based RDF data management // Zoi Kaoudim, Ioana Manolescu, Stamatis Zampetakis.
Author:	Kaoudim, Zoi,
other author:	Manolescu, Ioana,
Description:	1 PDF (xii, 91 pages) :illustrations (some color). :
Notes:	Part of: Synthesis digital library of engineering and computer science.
Subject:	Database management. -
Online resource:	https://doi.org/10.2200/S00986ED1V01Y202001DTM062
Online resource:	https://ieeexplore.ieee.org/servlet/opac?bknumber=9036324
ISBN:	9781681730349

Cloud-based RDF data management /
Kaoudim, Zoi,

Cloud-based RDF data management /Zoi Kaoudim, Ioana Manolescu, Stamatis Zampetakis. - 1 PDF (xii, 91 pages) :illustrations (some color). - Synthesis lectures on data nanagement,#622153-5426 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 75-89).

1. Introduction -- 2. Preliminaries -- 2.1. Resource Description Framework (RDF) -- 2.2. Distributed storage and computing paradigms -- 2.3. Summary

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

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.

Mode of access: World Wide Web.

ISBN: 9781681730349

Standard No.: 10.2200/S00986ED1V01Y202001DTM062doiSubjects--Topical Terms:

557799
Database management.
Subjects--Index Terms:

RDFIndex Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: QA76.9.D3 / K363 2020eb

Dewey Class. No.: 005.74

Cloud-based RDF data management /
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100 1 $a Kaoudim, Zoi, $e author. $3 1253159
245 1 0 $a Cloud-based RDF data management / $c Zoi Kaoudim, Ioana Manolescu, Stamatis Zampetakis.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c [2020]
300 $a 1 PDF (xii, 91 pages) : $b illustrations (some color).
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337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
490 1 $a Synthesis lectures on data nanagement, $x 2153-5426 ; $v #62
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 75-89).
505 0 $a 1. Introduction -- 2. Preliminaries -- 2.1. Resource Description Framework (RDF) -- 2.2. Distributed storage and computing paradigms -- 2.3. Summary
505 8 $a 3. Cloud-based RDF storage -- 3.1. Partitioning strategies -- 3.2. Storing in distributed file systems -- 3.3. Storing in key-value stores -- 3.4. Storing in multiple centralized RDF stores -- 3.5. Storing in main memory stores -- 3.6. Storing in multiple back-end stores -- 3.7. Summary
505 8 $a 4. Cloud-based SPARQL query processing -- 4.1. Relational-based query processing -- 4.2. Graph-based query processing -- 4.3. Summary
505 8 $a 5. SPARQL query optimization for the cloud -- 5.1. Query plan search space -- 5.2. Planning algorithms -- 5.3. Summary
505 8 $a 6. RDFS reasoning in the cloud -- 6.1. Reasoning through RDFS closure computation -- 6.2. Reasoning through query reformulation -- 6.3. Hybrid techniques -- 6.4. Summary -- 7. Concluding remarks.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 $a 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.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on April 6, 2020).
650 0 $a Database management. $3 557799
650 0 $a RDF (Document markup language) $3 1133112
650 0 $a Cloud computing. $3 716205
653 $a RDF
653 $a cloud computing
653 $a MapReduce
653 $a key-value stores
653 $a query optimization
653 $a reasoning
655 0 $a Electronic books. $2 local $3 554714
700 1 $a Manolescu, Ioana, $e author. $3 1253160
700 1 $a Zampetakis, Stamatis, $e author. $3 1253161
776 0 8 $i Print version: $z 9781681737805 $z 9781681730332
830 0 $a Synthesis digital library of engineering and computer science. $3 598254
830 0 $a Synthesis lectures on data management ; $v #36. $3 931356
856 4 0 $3 Abstract with links to full text $u https://doi.org/10.2200/S00986ED1V01Y202001DTM062
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=9036324