SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Proceedings of the 17th International Conference on Data Engineering
Introduction to Operations Research and Revised CD-ROM 8
Introduction to Operations Research and Revised CD-ROM 8
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On dominating your neighborhood profitably
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Dominant Graph: An Efficient Indexing Structure to Answer Top-K Queries
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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Skyline queries have gained attention as an effective way to identify desirable objects that are "not dominated" by another object in the dataset. From market perspective, such objects are favored as pareto-optimal choices, as each of such objects has at least one competitive edge against all other objects, or not dominated. In other words, non-skyline objects have room for pareto-optimal improvements for more favorable positioning in the market. The goal of this paper is, for such non-skyline objects, to identify the cost-minimal pareto-optimal improvement strategy. More specifically, we abstract this problem as a mixed integer programming problem and develop a novel algorithm for efficiently identifying the optimal solution. In addition, the problem can be reversed to identify, for a skyline product, top-k threats that can be competitors after pareto-optimal improvements with the k lowest costs. Through extensive experiments using synthetic and real-life datasets, we show that our proposed framework is both efficient and scalable.