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This paper proposes a category- and selection-enabled nearest neighbor join (NNJ) between relation r and relation s , with similarity on T and support for category attributes C and selection predicate θ . Our solution does not suffer from redundant fetches and index false hits , which are the main performance bottlenecks of current nearest neighbor join techniques. A category-enabled NNJ leverages the category attributes C for query evaluation. For example, the categories of relation r can be used to limit relation s accessed at most once. Solutions that are not category-enabled must process each category independently and end up fetching, either from disk or memory, the blocks of the input relations multiple times. A selection-enabled NNJ performs well independent of whether the DBMS optimizer pushes the selection down or evaluates it on the fly. In contrast, index-based solutions suffer from many index false hits or end up in an expensive nested loop. Our solution does not constrain the physical design, and is efficient for row- as well as column-stores. Current solutions for column-stores use late materialization, which is only efficient if the data is clustered on the category attributes C . Our evaluation algorithm finds, for each outer tuple r , the inner tuples that satisfy the equality on the category and have the smallest distance to r with only one scan of both inputs. We experimentally evaluate our solution using a data warehouse that manages analyses of animal feeds. [ABSTRACT FROM AUTHOR]