The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast linear expected-time alogorithms for computing maxima and convex hulls
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Proceedings of the 17th International Conference on Data Engineering
Mining thick skylines over large databases
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
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
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Approximately dominating representatives
Theoretical Computer Science
Continuous Processing of Preference Queries in Data Streams
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Regret-minimizing representative databases
Proceedings of the VLDB Endowment
Interactive regret minimization
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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Pareto dominance plays an important role in diverse application domains such as economics and e-commerce, and it is widely being used in multicriteria decision making. In these cases, objectives are usually contradictory and therefore it is not straightforward to provide a set of items that are the "best" according to the user's preferences. Skyline queries have been extensively used to recommend the most dominant items. However, in some cases skyline items are either too few, or too many, causing problems in selecting the prevailing ones. The number of skyline items depend heavily on both the data distribution, the data population and the dimensionality of the data set. In this work, we provide a dominance-based analysis and querying scheme that aims at alleviating the skyline cardinality problem, trying to introduce ranking on the items. The proposed scheme can be used either as a mining or as a querying tool, helping the user in selecting the mostly preferred items. Performance evaluation based on different distributions, populations and dimensionalities show the effectiveness of the proposed scheme