Effectiveness of NAQ-tree in handling reverse nearest-neighbor queries in high-dimensional metric space

Reverse nearest-neighbor (RNN) query processing is important for many applications such as decision-support systems, profile-based marketing and molecular biology; consequently, RNN query processing has attracted considerable attention in the research community in recent years. Most existing approaches for RNN query processing either rely on nearest-neighbor pre-computation or work for specific data space (e.g., the Euclidean space). The only method for RNN query processing in metric space is based on the M-tree. In this paper, we propose an approach for RNN query processing in high-dimensional metric space using distance-based index structure (in particular, NAQ-tree that outperforms the other distance-based index structures as we have already verified in a previous study). In high-dimensional space, the properties of distance-based index structure provide strong pruning rules than the M-tree. In addition, unlike the previous work, our approach integrates the filtering and verification steps and uses the information obtained in the verification stage to further improve the filtering rate. Our approach delivers results incrementally and hence well serves real-time applications. The reported experimental results demonstrate the applicability and effectiveness of the proposed NAQ-tree-based RNN approach.