Relative customer preferences for feature combinations have long been represented in multi-dimensional space. In fact, the whole area of multi-dimensional scaling arose from this application.

Skyline queries over multi-dimensional data sets have, justifiably, become a topic of intense study in recent years. The set of skyline points present a scale-free choice of data points worthy of further consideration in many contexts.

This paper, in a very clever way, ties these two ideas together. Three new classes of skyline queries are defined, to help firms delineate market opportunities based on customer preferences and competitive products. Efficient computation for such queries is achieved through a novel data structure.