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VLDB
2007
ACM

Efficient Skyline Computation over Low-Cardinality Domains

11 years 1 months ago
Efficient Skyline Computation over Low-Cardinality Domains
Current skyline evaluation techniques follow a common paradigm that eliminates data elements from skyline consideration by finding other elements in the dataset that dominate them. The performance of such techniques is heavily influenced by the underlying data distribution (i.e. whether the dataset attributes are correlated, independent, or anti-correlated). In this paper, we propose the Lattice Skyline Algorithm (LS) that is built around a new paradigm for skyline evaluation on datasets with attributes that are drawn from low-cardinality domains. LS continues to apply even if one attribute has high cardinality. Many skyline applications naturally have such data characteristics, and previous skyline methods have not exploited this property. We show that for typical dimensionalities, the complexity of LS is linear in the number of input tuples. Furthermore, we show that the performance of LS is independent of the input data distribution. Finally, we demonstrate through extensive experi...
Michael D. Morse, Jignesh M. Patel, H. V. Jagadish
Added 05 Dec 2009
Updated 05 Dec 2009
Type Conference
Year 2007
Where VLDB
Authors Michael D. Morse, Jignesh M. Patel, H. V. Jagadish
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