Cloud-Based RDF Data Management / by Zoi Kaoudi, Ioana Manolescu, Stamatis Zampetakis
By: Kaoudi, Zoi, autor
Contributor(s): Manolescu, Ioana, autor
| Zampetakis, Stamatis, autor
Material type:
E-bookSeries: (Synthesis Lectures on Data Management, 2153-5426).Publisher: Cham : Springer International Publishing, 2020Edition: 1st edition 2020.Description: 1 recurso en línea (XII, 91 páginas).ISBN: 9783031018756.Subject: Informática en la nube
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | QA76.76.H94 2020 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.01112949 |
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| QA76.76.E95 T447 2016 EB The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt | QA76.76.F34 E464 2018 EB Software Failure Investigation A Near-Miss Analysis Approach | QA76.76.H94 2009 EB XML Retrieval | QA76.76.H94 2020 EB Cloud-Based RDF Data Management | QA76.76.H94 2022 EB Model-Driven Development of Akoma Ntoso Application Profiles : A Conceptual Framework for Model-Based Generation of XML Subschemas | QA76.76.H94 C56 2016 EB DITA the Topic-Based XML Standard : A Quick Start | QA76.76.H94 F73 2012 EB Responsive Web design with HTML5 and CSS3 : learn responsive design using HTML5 and CSS3 to adapt websites to any browser or screen size |
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.
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.
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