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Anytime measures for top- k algorithms on exact and fuzzy data sets

Anytime measures for top- k algorithms on exact and fuzzy data sets Top- k queries on large multi-attribute data sets are fundamental operations in information retrieval and ranking applications. In this article, we initiate research on the anytime behavior of top- k algorithms on exact and fuzzy data. In particular, given specific top- k algorithms (TA and TA-Sorted) we are interested in studying their progress toward identification of the correct result at any point during the algorithms’ execution. We adopt a probabilistic approach where we seek to report at any point of operation of the algorithm the confidence that the top- k result has been identified. Such a functionality can be a valuable asset when one is interested in reducing the runtime cost of top- k computations. We present a thorough experimental evaluation to validate our techniques using both synthetic and real data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Anytime measures for top- k algorithms on exact and fuzzy data sets

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References (37)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
DOI
10.1007/s00778-008-0127-9
Publisher site
See Article on Publisher Site

Abstract

Top- k queries on large multi-attribute data sets are fundamental operations in information retrieval and ranking applications. In this article, we initiate research on the anytime behavior of top- k algorithms on exact and fuzzy data. In particular, given specific top- k algorithms (TA and TA-Sorted) we are interested in studying their progress toward identification of the correct result at any point during the algorithms’ execution. We adopt a probabilistic approach where we seek to report at any point of operation of the algorithm the confidence that the top- k result has been identified. Such a functionality can be a valuable asset when one is interested in reducing the runtime cost of top- k computations. We present a thorough experimental evaluation to validate our techniques using both synthetic and real data sets.

Journal

The VLDB JournalSpringer Journals

Published: Apr 1, 2009

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