Distributed top-k aggregation queries at large

Neumann, T., Bender, M., Michel, S., Schenkel, R., Triantafillou, P. and Weikum, G. (2009) Distributed top-k aggregation queries at large. Distributed and Parallel Databases, 26(1), pp. 3-27. (doi: 10.1007/s10619-009-7041-z)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Triantafillou, Professor Peter
Authors: Neumann, T., Bender, M., Michel, S., Schenkel, R., Triantafillou, P., and Weikum, G.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Distributed and Parallel Databases
ISSN (Online):1573-7578
Published Online:18 June 2009
Copyright Holders:Copyright © 2009 The Authors
First Published:First published in Distributed and Parallel Databases 26(1):3-27
Publisher Policy:Reproduced under a Creative Commons License

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