We extend the capability of a skyline query to solve the market positioning evaluation of the dominated points of products. This capability can assist both manufacturer and customer to plan product features according to their approximate distances to the preference points. For this purpose we develop a distance measurement on a convex skyline approach. First, we present data sets contain record of multidimensional product, where every dimension represents one attribute of product feature. Then we evaluate the skyline query of a data set and divide the data set into a collection of preferable objects in skyline and another are the dominated points. Here we assume that each dominated point is potentially entering the preferable region by moving their attribute values into customer preference’s points. We provide the query to find potential products to enter the skyline with a lower additional distance (as cost). This approach compute minimum additional cost to revive the dominated points based on a user’s elicitation of a maximum threshold. Results of our comprehensive experiments show the effectiveness of this approach both in real world and synthetic data sets.

skyline query, dominated points, customer preference