  When we have two data sets and want to find the nearest neighbour of each
point in the first dataset among points in the second one, we need the all
nearest neighbour operator. This is an operator in spatial databases that has
many application in different fields such as GIS and VLSI circuit design.
Existing algorithms for calculating this operator assume that there is no pre
computation on these data sets. These algorithms has o(n*m*d) time complexity
where n and m are the number of points in two data sets and d is the dimension
of data points. With assumption of some pre computation on data sets algorithms
with lower time complexity can be obtained. One of the most common pre
computation on spatial data is Delaunay graphs. In the Delaunay graph of a data
set each point is linked to its nearest neighbours. In this paper, we introduce
an algorithm for computing the all nearest neighbour operator on spatial data
sets based on their Delaunay graphs. The performance of this algorithm is
compared with one of the best existing algorithms for computing ANN operator in
terms of CPU time and the number of IOs. The experimental results show that
this algorithm has better performance than the other.
