000			a
999			_c30118 _d30118
008			210205b xxu||||| |||| 00| 0 eng d
020			_a9781681730332
082			_a005.74 _bKAO
100			_aKaoudim, Zoi
245			_aCloud-based RDF data management
260			_bMorgan & Claypool _c2020 _aSan Rafael
300			_a91 p. _bill. _c24 cm
365			_d76.80 _b39.95 _cUSD
490			_aSynthesis lectures on data nanagement _v2153-5426 ; #62
504			_aIncludes bibliographical references
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			_aRDF
650			_aCloud computing
650			_aDatabase management
650			_akey-value stores
650			_aQuery optimization
650			_aDocument markup language
710			_aManolescu, Ioana
710			_aZampetakis, Stamatis
942			_2ddc _cBK