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. Keywords Approximate query ? Anytime ? Top-k ? Fuzzy data

• Type: Conference
• Year: 2009
• Where: VLDB
• Authors: Benjamin Arai, Gautam Das, Dimitrios Gunopulos, Nick Koudas
• Keywords: Database, Fuzzy Data, Multi-attribute Data Sets, Real Data Sets, VLDB 2009