Skyline computation has many applications including multi-criteria decision
making. In this talk, we investigate the problem of selecting k
skyline points so that the number of points, which are dominated by at least
one of these k skyline points, is maximized.
We first present an efficient dynamic programming based exact
algorithm in a 2d-space.
Then, we show that the problem is NP-hard when the dimensionality
is $3$ or more and it can be approximately solved by a polynomial time
algorithm with the guaranteed approximation ratio 1 - 1/e.
To speed-up the computation, an efficient, scalable, index-based
randomized algorithm is developed by applying a probabilistic
A comprehensive performance evaluation demonstrates that our
randomized technique is very efficient, highly accurate, and scalable.
(Joint work with Yidong Yuan, Qing Zhang, and Ying Zhang)
Xuemin Lin is an Associate Professor (reader) in the School of Computer
Science and Engineering, the University of New South Wales. He has been
the head of database research group since 2001. Before joining UNSW,
Xuemin held various academic positions at University of Queensland and
University of Western Australia. He also taught at the Chinese
University of Hong Kong in 2000. Dr. Lin got his PhD in Computer Science
from the University of Queensland in 1992 and his BSc in Applied Math
from Fudan University in 1984. During 1984-1988, he studied for PhD in
Applied Math at Fudan University.
His current research interests lie in data streams, approximate query
processing, data streams, spatial data analysis, and graph visualization.
He has published about 100 research papers in theory and DB societies
including TODS, TKDE, Algorithmic, Theoretical Computer Science, VLDB,
ICDE, EDBT, etc. Xuemin serves as PC (or PC-co chairs) in
a number of conferences in database systems and algorithms.