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.
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Notes
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In this paper we use the terms k-NN trajectories and top-k facilities interchangeably as we use different types of distances or weighted distances as a scoring function to find the best k facilities.
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Al Rahat, T., Arman, A., Ali, M.E. (2018). Maximizing Reverse k-Nearest Neighbors for Trajectories. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_21
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