Computational geometry: an introduction
Computational geometry: an introduction
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Proceedings of the 17th International Conference on Data Engineering
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Efficient computation of the skyline cube
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Catching the best views of skyline: a semantic approach based on decisive subspaces
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Maintaining Sliding Window Skylines on Data Streams
IEEE Transactions on Knowledge and Data Engineering
Refreshing the sky: the compressed skycube with efficient support for frequent updates
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Towards efficient dominant relationship exploration of the product items on the web
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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Due to the importance of skyline query in many applications, it has been attracted much attention recently. Given an N-dimensional dataset D, a point p is said to dominate another point q if p is better than q in at least one dimension and equal to or better than q in the remaining dimensions. Recently, Li et al. [9] proposed to analyze more general dominant relationship in a business model that, users are more interested in the details of the dominant relationship in a dataset, i.e., a point p dominates how many other points. In this paper, we further generalize this problem that, users are more interested in whom these dominated points are. We show that the framework proposed in [9] can not efficiently solve this problem. We find the interrelated connection between the partial order and the dominant relationship. Based on this discovery, we propose efficient algorithms to answer the general dominant relationship queries by querying the partial order representation of spatial datasets. Extensive experiments illustrate the effectiveness and efficiency of our methods.