Ranking strategies and threats: a cost-based pareto optimization approach

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.