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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.
The VLDB Journal – Springer Journals
Published: Apr 1, 2009
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