Research Topic - Skyline Query Processing

1)   This project started in January 2005, and is funded by the FRC. The goal of this project is to investigate efficient techniques to process an important class of qualitative preference queries called skyline queries. A skyline query returns a set of records such that each returned record is not dominated by other records with respect to a user-specified set of attributes. 

The project has so far achieved the following:

a)      We have developed a novel framework and several scalable algorithms to efficiently process a generalized class of skyline queries that support data attributes with partially ordered domains.

b)     We have introduced two novel notions of interesting skyline points (frequent skylines and k-dominant skylines) and developed efficient algorithms to compute them. These concepts are particularly important for high-dimensional data points to enable a meaningful ranking of the large set of skyline query results.

2) Some Publications

C.Y. Chan, P.K. Eng, K.L. Tan, ``Stratified Computation of Skyline Queries with Partially-Ordered Domains'', ACM SIGMOD Conference, Baltimore, Maryland, June 2005, 12 pages.

C.Y. Chan, H.V. Jagadish, K.L. Tan, Anthony K.H. Tung, Z. Zhang,  ``On High-dimensional skylines'', EDBT Conference, Munich, Germany, March 2006, 18 pages.

C.Y. Chan, H.V. Jagadish, K.L. Tan, Anthony K.H. Tung, Z. Zhang,  ``Finding k-Dominant Skylines in High Dimensional Space'', ACM SIGMOD Conference, Chicago, Illinois, June 2006, 12 pages.

C. Li, B.C. Ooi, Anthony K. H. Tung, S. Wang, ``DADA: A Data Cube for Dominant Relationship Analysis'', ACM SIGMOD Conference, Chicago, Illinois, June 2006, 12 pages.

3) List of Collaborations with:

H.V. Jagadish, University of Michigan

4) Names of the Faculty Members in the research area

Chee Yong Chan, Beng Chin Ooi, Kian-Lee Tan, and Anthony Tung