Principles of Information Filtering in Metric Spaces

The traditional problem of similarity search requires to find, within a set of points, those that are closer to a query point q, according to a distance function d. In this paper we introduce the novel problem of metric filtering: in this scenario, each data point x_i possesses its own distance function d_i and the task is to find those points that are close enough, according to d_i, to a query point q. This minor difference in the problem formulation introduces a series of challenges from the point of view of efficient evaluation. We provide basic definitions and alternative pivot-based resolution strategies, presenting results from a preliminary experimentation that show how the proposed solutions are indeed effective in reducing evaluation costs.