  Given a point query Q in multi-dimensional space, K-Nearest Neighbor (KNN)
queries return the K closest answers according to given distance metric in the
database with respect to Q. In this scenario, it is possible that a majority of
the answers may be very similar to some other, especially when the data has
clusters. For a variety of applications, such homogeneous result sets may not
add value to the user. In this paper, we consider the problem of providing
diversity in the results of KNN queries, that is, to produce the closest result
set such that each answer is sufficiently different from the rest. We first
propose a user-tunable definition of diversity, and then present an algorithm,
called MOTLEY, for producing a diverse result set as per this definition.
Through a detailed experimental evaluation on real and synthetic data, we show
that MOTLEY can produce diverse result sets by reading only a small fraction of
the tuples in the database. Further, it imposes no additional overhead on the
evaluation of traditional KNN queries, thereby providing a seamless interface
between diversity and distance.
