  User preference queries are very important in spatial databases. With the
help of these queries, one can found best location among points saved in
database. In many situation users evaluate quality of a location with its
distance from its nearest neighbor among a special set of points. There has
been less attention about evaluating a location with its distance to nearest
neighbors in spatial user preference queries. This problem has application in
many domains such as service recommendation systems and investment planning.
Related works in this field are based on top-k queries. The problem with top-k
queries is that user must set weights for attributes and a function for
aggregating them. This is hard for him in most cases. In this paper a new type
of user preference queries called spatial nearest neighbor skyline queries will
be introduced in which user has some sets of points as query parameters. For
each point in database attributes are its distances to the nearest neighbors
from each set of query points. By separating this query as a subset of dynamic
skyline queries N2S2 algorithm is provided for computing it. This algorithm has
good performance compared with the general branch and bound algorithm for
skyline queries.
