Processing Class-Constraint K-NN Queries with MISP

In this work, we consider processing k-nearest-neighbor (k-NN) queries, with the additional requirement that the result objects are of a specific type. To solve this problem, we propose an approach based on a combination of an inverted index and state-of-the-art similarity search index structure for efficiently pruning the search space early-on. Furthermore, we provide a cost model, and an extensive experimental study, that analyzes the performance of the proposed index structure under different configurations, with the aim of finding the most efficient one for the dataset being searched.