Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions
In this paper, the authors formulate a top-k query that compares objects in a database to a user-provided query object on a novel scoring function. The proposed scoring function combines the idea of attractive and repulsive dimensions into a general framework to overcome the weakness of traditional distance or similarity measures. They study the properties of the proposed class of scoring functions and develop efficient and scalable index structures that index the isolines of the function. They demonstrate various scenarios where the query finds application. Empirical evaluation demonstrates a performance gain of one to two orders of magnitude on querying time over existing state-of-the-art top-k techniques.