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008			220601s2020 sz | s |||| 0|eng d
020			_a9783031018756
024	7		_a10.1007/978-3-031-01875-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.76.H94 _b2020 EB
100	1		_aKaoudi, Zoi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687888
245	1	0	_aCloud-Based RDF Data Management _cby Zoi Kaoudi, Ioana Manolescu, Stamatis Zampetakis
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XII, 91 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9666069 _aInformática en la nube
650		7	_2embne _9150569 _aSistemas de gestión de bases de datos
650		7	_2embne _9687891 _aRDF (Lenguaje de marcas)
700	1		_aManolescu, Ioana _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687889
700	1		_aZampetakis, Stamatis _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687890
776	0	8	_iPrinted edition: _z9783031001024
776	0	8	_iPrinted edition: _z9783031007477
776	0	8	_iPrinted edition: _z9783031030031
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01875-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI