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)
Multidimensional divide-and-conquer
Communications of the ACM
Multidimensional binary search trees used for associative searching
Communications of the ACM
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
Robust Cardinality and Cost Estimation for Skyline Operator
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Algorithms and analyses for maximal vector computation
The VLDB Journal — The International Journal on Very Large Data Bases
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient sort-based skyline evaluation
ACM Transactions on Database Systems (TODS)
Scalable skyline computation using object-based space partitioning
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Kernel-based skyline cardinality estimation
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Randomized multi-pass streaming skyline algorithms
Proceedings of the VLDB Endowment
BSkyTree: scalable skyline computation using a balanced pivot selection
Proceedings of the 13th International Conference on Extending Database Technology
Computing Large Skylines over Few Dimensions: The Curse of Anti-correlation
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
On finding skylines in external memory
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Hi-index | 0.00 |
Finding the skyline in a multi-dimensional space is relevant to a wide range of applications. The skyline operator over a set of d-dimensional points selects the points that are not dominated by any other point on all dimensions. Therefore, it provides a minimal set of candidates for the users to make their personal trade-off among all optimal solutions. The existing algorithms establish both the worst case complexity by discarding distributions and the average case complexity by assuming dimensional independence. However, the data in the real world is more likely to be anti-correlated. The cardinality and complexity analysis on dimensionally independent data is meaningless when dealing with anti-correlated data. Furthermore, the performance of the existing algorithms becomes impractical on anti-correlated data. In this paper, we establish a cardinality model for anti-correlated distributions. We propose an accurate polynomial estimation for the expected value of the skyline cardinality. Because the high skyline cardinality downgrades the performance of most existing algorithms on anti-correlated data, we further develop a determination and elimination framework which extends the well-adopted elimination strategy. It achieves remarkable effectiveness and efficiency. The comprehensive experiments on both real datasets and benchmark synthetic datasets demonstrate that our approach significantly outperforms the state-of-the-art algorithms under a wide range of settings.