Lower bounds for orthogonal range searching: I. The reporting case
Journal of the ACM (JACM)
Lower bounds for orthogonal range searching: part II. The arithmetic model
Journal of the ACM (JACM)
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A framework for expressing and combining preferences
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Querying high-dimensional data in single-dimensional space
The VLDB Journal — The International Journal on Very Large Data Bases
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Stratified computation of skylines with partially-ordered domains
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Preference SQL: design, implementation, experiences
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient skyline computation over low-cardinality domains
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Approaching the skyline in Z order
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Monochromatic and bichromatic reverse skyline search over uncertain databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Angle-based space partitioning for efficient parallel skyline computation
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Topologically Sorted Skylines for Partially Ordered Domains
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
An efficient skyline framework for matchmaking applications
Journal of Network and Computer Applications
Representing and reasoning with qualitative preferences for compositional systems
Journal of Artificial Intelligence Research
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We devise a skyline algorithm that can efficiently mitigate the enormous overhead of processing millions of tuples on totally- and partially-ordered domains (henceforth, TODs and PODs). With massive datasets, existing techniques spend a significant amount of time on a dominance comparison because of both a large number of skyline points and the unprogressive method of skyline computing with PODs. (If data has high dimensionality, the situation is undoubtedly aggravated.) The progressiveness property turns out to be the key feature for solving all remaining problems. This article presents a FAST-SKY algorithm that deals successfully with these two obstacles and improves skyline query processing time strikingly, even with high-dimensional data. Progressive skyline evaluation with PODs is guaranteed by new index structures and topological sorting order. A stratification technique is adopted to index data on PODs, and we propose two new index structures: stratified R-trees (SR-trees) for low-dimensional data and stratified MinMax treaps (SM-treaps) for high-dimensional data. A fast dominance comparison is achieved by using a reporting query instead of a dominance query, and a dimensionality reduction technique. Experimental results suggest that in general cases (anti-correlated and uniform distributions) FAST-SKY is orders of magnitude faster than existing algorithms.