Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th 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
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
SUBSKY: Efficient Computation of Skylines in Subspaces
ICDE '06 Proceedings of the 22nd International Conference on 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
Algorithms and analyses for maximal vector computation
The VLDB Journal — The International Journal on Very Large Data Bases
Towards multidimensional subspace skyline analysis
ACM Transactions on Database Systems (TODS)
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Scalable skyline computation using object-based space partitioning
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
BSkyTree: scalable skyline computation using a balanced pivot selection
Proceedings of the 13th International Conference on Extending Database Technology
Z-SKY: an efficient skyline query processing framework based on Z-order
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the VLDB Endowment
Online subspace skyline query processing using the compressed skycube
ACM Transactions on Database Systems (TODS)
Toward efficient multidimensional subspace skyline computation
The VLDB Journal — The International Journal on Very Large Data Bases
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Skyline queries have gained considerable attention for multi-criteria analysis of large-scale datasets. However, the skyline queries are known to return too many results for high-dimensional data. To address this problem, a skycube is introduced to efficiently provide users with multiple skylines with different strengths. For efficient skycube construction, state-of-the-art algorithms amortized redundant computation among subspace skylines, or cuboids, either (1) in a bottom-up fashion with the principle of sharing result or (2) in a top-down fashion with the principle of sharing structure. However, we observed further room for optimization in both principles. This paper thus aims to design a more efficient skycube algorithm that shares multiple cuboids using more effective structures. Specifically, we first develop each principle by leveraging multiple parents and a skytree, representing recursive point-based space partitioning. We then design an efficient algorithm exploiting these principles. Experimental results demonstrate that our proposed algorithm is significantly faster than state-of-the-art skycube algorithms in extensive datasets.