Cloud-Based RDF Data Management (Record no. 85307)

000 -LEADER
fixed length control field 04167nam a22005295i 4500
001 - CONTROL NUMBER
control field 978-3-031-01875-6
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730164115.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2020 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031018756
-- 978-3-031-01875-6
082 04 - CLASSIFICATION NUMBER
Call Number 004.6
100 1# - AUTHOR NAME
Author Kaoudi, Zoi.
245 10 - TITLE STATEMENT
Title Cloud-Based RDF Data Management
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2020.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XII, 91 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Data Management,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- Preliminaries -- Cloud-Based RDF Storage -- Cloud-Based SPARQL Query Processing -- SPARQL Query Optimization for the Cloud -- RDFS Reasoning in the Cloud -- Concluding Remarks -- Bibliography -- Authors' Biographies.
520 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Manolescu, Ioana.
700 1# - AUTHOR 2
Author 2 Zampetakis, Stamatis.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01875-6
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2020.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer networks .
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data structures (Computer science).
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information theory.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Communication Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Structures and Information Theory.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2153-5426
912 ## -
-- ZDB-2-SXSC

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