Proceedings of the 17th International Conference on Data Engineering
Indexing for progressive skyline computation
Data & Knowledge Engineering
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Efficient Skyline and Top-k Retrieval in Subspaces
IEEE Transactions on Knowledge and Data Engineering
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
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
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
Efficiently Producing the K Nearest Neighbors in the Skyline on Vertically Partitioned Tables
International Journal of Information Retrieval Research
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Skyline queries have become commonplace in many applications. The main problem is to efficiently find the set of Pareto-optimal choices from a large amount of database items. Several algorithms and indexing techniques have been proposed recently, but until now no indexing technique was able to address all problems for skyline queries in realistic applications: fast access, superior scalability even for higher dimensions, and low costs for maintenance in face of data updates. In this paper we design and evaluate a trie-based indexing technique that solves the major efficiency bottlenecks of skyline queries. It scales gracefully even for high dimensional queries, is largely independent of the underlying data distributions, and allows for efficient updates. Our experiments on real and synthetic datasets show a performance increase of up to two orders of magnitude compared to previous indexing techniques.