Abstract
Group nearest neighbor query(GNN for short) is a classic problem in the spatial database field. Given a data point set D, a query point set Q, the goal of the Group nearest neighbor query(GNN) is to select an object point o in D to minimize the total distance between o and all query points in Q. In this paper, we study GNN in the streaming data environment, i.e., continuous group nearest neighbor search(CGNN for short) over sliding window. In this paper, we propose a continuous query processing framework named BGPT. The idea of the framework is to partition the window and prune some meaningless objects through the dominant relationship between partitions. In order to efficiently support CGNN, we propose a grid-based index to manage streaming data. At the same time, we propose a partition-based method that can use a small number of objects in the streaming data set to monitor query result object. The comprehensive experiments on both real and synthetic data sets demonstrate the superiority of both efficiency and quality.
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Acknowledgements
This paper is partly supported by the National Key Research and Development Program of China(2020YFB1707901), the National Natural Science Foundation of Liao Ning(2022-MS-303, 2022-MS-302, and 2022-BS-218).
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Zhu, R., Li, C., Meng, X., Zong, C., Qiu, T. (2023). Continuous Group Nearest Neighbor Query over Sliding Window. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14180. Springer, Cham. https://doi.org/10.1007/978-3-031-46677-9_16
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DOI: https://doi.org/10.1007/978-3-031-46677-9_16
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