Distributed top-k aggregation queries at large

Abstract Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods f...
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Autor*in:	Neumann, Thomas [verfasserIn] Bender, Matthias Michel, Sebastian Schenkel, Ralf Triantafillou, Peter Weikum, Gerhard

Format:	Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Top- Distributed queries Query optimization Cost models

Anmerkung:	© The Author(s) 2009

Übergeordnetes Werk:	Enthalten in: Distributed and parallel databases - Springer US, 1993, 26(2009), 1 vom: 18. Juni, Seite 3-27
Übergeordnetes Werk:	volume:26 ; year:2009 ; number:1 ; day:18 ; month:06 ; pages:3-27

Links:	Volltext

DOI / URN:	10.1007/s10619-009-7041-z

Katalog-ID:	OLC2027066659

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author_browse	Neumann, Thomas Bender, Matthias Michel, Sebastian Schenkel, Ralf Triantafillou, Peter Weikum, Gerhard
container_volume	26
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format_se	Aufsätze
author-letter	Neumann, Thomas
doi_str_mv	10.1007/s10619-009-7041-z
dewey-full	070 020 004
title_sort	distributed top-k aggregation queries at large
title_auth	Distributed top-k aggregation queries at large
abstract	Abstract Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network. © The Author(s) 2009
abstractGer	Abstract Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network. © The Author(s) 2009
abstract_unstemmed	Abstract Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network. © The Author(s) 2009
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container_issue	1
title_short	Distributed top-k aggregation queries at large
url	https://doi.org/10.1007/s10619-009-7041-z
remote_bool	false
author2	Bender, Matthias Michel, Sebastian Schenkel, Ralf Triantafillou, Peter Weikum, Gerhard
author2Str	Bender, Matthias Michel, Sebastian Schenkel, Ralf Triantafillou, Peter Weikum, Gerhard
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doi_str	10.1007/s10619-009-7041-z
up_date	2024-07-03T13:39:08.575Z
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