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EDBT

2006

ACM

2006

ACM

In many decision-making applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset. In a high-dimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper, we introduce a novel metric, called skyline frequency that compares and ranks the interestingness of data points based on how often they are returned in the skyline when different number of dimensions (i.e., subspaces) are considered. Intuitively, a point with a high skyline frequency is more interesting as it can be dominated on fewer combinations of the dimensions. Thus, the problem becomes one of finding top-k frequent skyline points. But the algorithms thus far proposed for skyline computation typically do not scale well with dimensionality. Moreover, frequent skyline computation requires that skylines be computed for each of an exponential number of subsets of the dimensions. We present...

Related Content

Added |
08 Dec 2009 |

Updated |
08 Dec 2009 |

Type |
Conference |

Year |
2006 |

Where |
EDBT |

Authors |
Chee Yong Chan, H. V. Jagadish, Kian-Lee Tan, Anthony K. H. Tung, Zhenjie Zhang |

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