Maximizing Reverse k-Nearest Neighbors for Trajectories

TLDR: This paper introduces a generic similarity measure between a query object and a data object that helps to define the nearest neighbors according to user requirements, and proposes some pruning strategies that can quickly compute k-NNs (or top-k) facility trajectories for a given user trajectory.

Abstract: In this paper, we address a popular query involving trajectories, namely, the Maximizing Reverse k-Nearest Neighbors for Trajectories (MaxRkNNT) query. Given a set of existing facility trajectories (e.g., bus routes), a set of user trajectories (e.g., daily commuting routes of users) and a set of query facility trajectories (e.g., proposed new bus routes), the MaxRkNNT query finds the proposed facility trajectory that maximizes the cardinality of reverse k-Nearest Neighbors (NNs) set for the query trajectories. A major challenge in solving this problem is to deal with complex computation of nearest neighbors (or similarities) with respect to multi-point queries and data objects. To address this problem, we first introduce a generic similarity measure between a query object and a data object that helps us to define the nearest neighbors according to user requirements. Then, we propose some pruning strategies that can quickly compute k-NNs (or top-k) facility trajectories for a given user trajectory. Finally, we propose a filter and refinement technique to compute the MaxRkNNT. Our experimental results show that our proposed approach significantly outperforms the baseline for both real and synthetic datasets.