  In this paper, we 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. We study the properties of the proposed class of scoring
functions and develop efficient and scalable index structures that index the
isolines of the function. We 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. Further, a qualitative analysis is performed on a real
dataset to highlight the potential of the proposed query in discovering hidden
data characteristics.
