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VLDB
2007
ACM

Anytime Measures for Top-k Algorithms

10 years 5 months ago
Anytime Measures for Top-k Algorithms
Top-k queries on large multi-attribute data sets are fundamental operations in information retrieval and ranking applications. In this paper, we initiate research on the anytime behavior of top-k algorithms. In particular, given specific top-k algorithms (TA and TASorted) 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.
Benjamin Arai, Gautam Das, Dimitrios Gunopulos, Ni
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where VLDB
Authors Benjamin Arai, Gautam Das, Dimitrios Gunopulos, Nick Koudas
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