[31 Oct 2005]
Speaker: Ken YIU
Venue: Room 308, Chow Yei Ching Building, HKU
Time: 4:00pm

DB Seminar Schedule

Efficient Quantile Retrieval on Multi-Dimensional Data

Speaker: Ken YIU

Abstract Given a set of $N$ multi-dimensional points, we study the computation of $\phi N$-quantiles according to a ranking function $F$, which is provided by the user at runtime. Specifically, $F$ computes a score based on the coordinates of each point; our objective is to report the object whose score is the $\phiN$-th smallest in the dataset. $\phi N$-quantiles provide a succinct summary about the $F$-distribution of the underlying data, which is useful for online decision support, data mining, selectivity estimation, query optimization, etc. Assuming that the dataset is indexed by a spatial access method, we propose several algorithms for retrieving a quantile efficiently. Analytical and experimental results demonstrate that a branch-and-bound method is highly effective in practice, outperforming alternative approaches by a significant factor.