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020 _a9783031018756
_9978-3-031-01875-6
024 7 _a10.1007/978-3-031-01875-6
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
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072 7 _aCOM043000
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072 7 _aUKN
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082 0 4 _a004.6
_223
100 1 _aKaoudi, Zoi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982162
245 1 0 _aCloud-Based RDF Data Management
_h[electronic resource] /
_cby Zoi Kaoudi, Ioana Manolescu, Stamatis Zampetakis.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXII, 91 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Management,
_x2153-5426
505 0 _aIntroduction -- 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 _aResource 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.
650 0 _aComputer networks .
_931572
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 1 4 _aComputer Communication Networks.
_982163
650 2 4 _aData Structures and Information Theory.
_931923
700 1 _aManolescu, Ioana.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982164
700 1 _aZampetakis, Stamatis.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982165
710 2 _aSpringerLink (Online service)
_982166
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001024
776 0 8 _iPrinted edition:
_z9783031007477
776 0 8 _iPrinted edition:
_z9783031030031
830 0 _aSynthesis Lectures on Data Management,
_x2153-5426
_982167
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01875-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c85307
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