SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
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
Standing Out in a Crowd: Selecting Attributes for Maximum Visibility
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 can be viewed as marketable, as each of such objects has at least one competitive edge against all the other objects, or not dominated. In other words, non-skyline objects are not marketable, as there always exists another product excelling in all the attributes. The goal of this paper is, for such non-skyline objects, to identify the cost-minimal enhancement to become a skyline point to gain marketability. More specifically, we abstract this problem as a mixed integer programming problem and develop a novel algorithm for efficiently identifying the optimal solution. Through extensive experiments using synthetic datasets, we show that our proposed framework is both efficient and scalable over extensive experiment settings.