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Peer-to-Peer Query Processing over Multidimensional Data [electronic resource] /by Akrivi Vlachou, Christos Doulkeridis, Kjetil Nørvåg, Yannis Kotidis.

by Vlachou, Akrivi [author.]; Doulkeridis, Christos [author.]; Nørvåg, Kjetil [author.]; Kotidis, Yannis [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: New York, NY : Springer New York, 2012.Description: XII, 84p. 18 illus. online resource.ISBN: 9781461421108.Subject(s): Engineering | Computer network architectures | Telecommunication | Engineering | Communications Engineering, Networks | Computer Systems Organization and Communication NetworksDDC classification: 621.382 Online resources: Click here to access online
Contents:
Introduction -- Peer-to-Peer Systems -- System Overview -- Query Operators -- Similarity Search in Metric Spaces -- Subspace Skyline Queries -- Top-k queries -- Summary -- References.
In: Springer eBooksSummary: Applications that require a high degree of distribution and loosely-coupled connectivity are ubiquitous in various domains, including scientific databases, bioinformatics, and multimedia retrieval. In all these applications, data is typically voluminous and multidimensional, and support for advanced query operators is required for effective querying and efficient processing. To address this challenge, we adopt a hybrid P2P architecture and propose novel indexing and query processing algorithms. We present a scalable framework that relies on data summaries that are distributed and maintained as multidimensional routing indices. Different types of data summaries enable efficient processing of a variety of advanced query operators.
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Introduction -- Peer-to-Peer Systems -- System Overview -- Query Operators -- Similarity Search in Metric Spaces -- Subspace Skyline Queries -- Top-k queries -- Summary -- References.

Applications that require a high degree of distribution and loosely-coupled connectivity are ubiquitous in various domains, including scientific databases, bioinformatics, and multimedia retrieval. In all these applications, data is typically voluminous and multidimensional, and support for advanced query operators is required for effective querying and efficient processing. To address this challenge, we adopt a hybrid P2P architecture and propose novel indexing and query processing algorithms. We present a scalable framework that relies on data summaries that are distributed and maintained as multidimensional routing indices. Different types of data summaries enable efficient processing of a variety of advanced query operators.

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