22nd International Conference on Data Engineering (ICDE'06) (2006)
Apr. 3, 2006 to Apr. 7, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2006.54
Nikos Mamoulis , University of Hong Kong
Kit Hung Cheng , University of Hong Kong
Man Lung Yiu , University of Hong Kong
David W. Cheung , University of Hong Kong
A top-k query combines different rankings of the same set of objects and returns the k objects with the highest combined score according to an aggregate function. We bring to light some key observations, which impose two phases that any top-k algorithm, based on sorted accesses, should go through. Based on them, we propose a new algorithm, which is designed to minimize the number of object accesses, the computational cost, and the memory requirements of top-k search. Adaptations of our algorithm for search variants (exact scores, on-line and incremental search, top-k joins, other aggregate functions, etc.) are also provided. Extensive experiments with synthetic and real data show that, compared to previous techniques, our method accesses fewer objects, while being orders of magnitude faster.
K. H. Cheng, D. W. Cheung, M. L. Yiu and N. Mamoulis, "Efficient Aggregation of Ranked Inputs," 22nd International Conference on Data Engineering (ICDE'06)(ICDE), Atlanta, Georgia, 2006, pp. 72.