Abstract
Location-based services recommend points of interests (POIs) which are nearer to the user’s position q. In practice, when the user is moving with a velocity \(\overrightarrow{v}\), he may prefer the nearer POIs which match his moving direction. In this paper, we propose the velocity-dependent nearest neighbor query (VeloNN query), which selects the POIs that are nearer and best match the user’s moving direction. In the VeloNN query, if the direction of a POI o highly matches the direction of \(\overrightarrow{v}\), o is likely to be preferred. Since computing the directional preferences of all POIs is time-consuming, we propose rules to filter out the POIs with low directional preferences. We also divide the space into tiles, i.e., rectangular areas, and compute a candidate set for each tile in advance. The VeloNN candidates can be quickly prepared after finding the tile where the user is. We conduct experiments on both synthetic and real datasets and the results show the proposed algorithms can support VeloNN queries efficiently.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61602031), the Fundamental Research Funds for the Central Universities (No. FRF-BD-19-012A, No. FRF-IDRY-19-023), and the National Key Research and Development Program of China (No. 2017YFB0202303).
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Miao, X., Guo, X., Yang, X., Yang, L., Wang, Z., Wulamu, A. (2021). Velocity-Dependent Nearest Neighbor Query. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_26
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DOI: https://doi.org/10.1007/978-3-030-85899-5_26
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