On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Location-aware privacy and more: a systems approach using context-aware database management systems
Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS
Call to order: a hierarchical browsing approach to eliciting users' preference
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
An efficient skyline framework for matchmaking applications
Journal of Network and Computer Applications
Progressive processing of subspace dominating queries
The VLDB Journal — The International Journal on Very Large Data Bases
Extract interesting skyline points in high dimension
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Transitivity-preserving skylines for partially ordered domains
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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Current interests in skyline computation arise due to their relation to preference queries. Since it is guaraneed that a skyline point will not lose out in all dimensions when compared to any other point in the data set, this means that for each skyline point, there exists a set of weight assignments to the dimensions such that the point will become the top user preference.We believe that the usefulness of skyline points is not limited to such application and can be extended to data analysis and knowledge discovery as well. However, since the skyline of high dimensional datasets (which are common in data analysis applications) can contain too many points, various means must be developed to filter off the less interesting skyline points in high dimensions. In this paper, we will propose algorithms to find a set of interesting skyline points called strong skyline points. Extensive experiments show that our proposal is both effective and efficient.