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

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in Computer SciencePublisher: New York, NY : Springer New York, 2012Description: XII, 84p. 18 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781461421108
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 621.382 23
LOC classification:
  • TK1-9971
Online resources:
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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Item type Current library Call number Status Date due Barcode
E-Book E-Book Central Library Available E-39813

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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