Before revision:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, the point sets P \in D that are closest in terms of bottleneck distance are returned .
After revision:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, we would like to find the point set(s) P \in \mathbb{D that are closest in terms of bottleneck distance are returned .