Discovering Relative Importance of Skyline Attributes

Querying databases with preferences is an important research problem. Among various approaches to querying with preferences, the skyline framework is one of the most popular. A well known deficiency of that framework is that all attributes are of the same importance in skyline preference relations. Consequently, the size of the results of skyline queries may grow exponentially with the number of skyline attributes. Here we propose the framework called p-skylines which enriches skylines with the notion of attribute importance. It turns out that incorporating relative attribute importance in skylines allows for reduction in the corresponding query result sizes. We propose an approach to discovering importance relationships of attributes, based on user-selected sets of superior and inferior examples. We show that the problem of checking the existence of and the problem of computing an optimal p-skyline preference relation covering a given set of examples are NP-complete and FNP-complete, respectively. However, we also show that a restricted version of the discovery problem – using only superior examples to discover attribute importance – can be solved efficiently in polynomial time. Our experiments show that the proposed importance discovery algorithm has high accuracy and good scalability.