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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baig, F., Teng, D., Kong, J., Wang, F.: Spear: dynamic spatio-temporal query processing over high velocity data streams. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2279–2284. IEEE (2021)
Koç, C.K.: Analysis of sliding window techniques for exponentiation. Comput. Math. Appl. 30(10), 17–24 (1995)
Kollios, G., Gunopulos, D., Tsotras, V.J.: Nearest neighbor queries in a mobile environment. In: Böhlen, M.H., Jensen, C.S., Scholl, M.O. (eds.) STDBM 1999. LNCS, vol. 1678, pp. 119–134. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48344-6_7
Kothuri, R.K.V., Ravada, S., Abugov, D.: Quadtree and r-tree indexes in oracle spatial: a comparison using gis data. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 546–557 (2002)
Li, T., Chen, L., Jensen, C.S., Pedersen, T.B.: Trace: real-time compression of streaming trajectories in road networks. Proc. VLDB Endowment 14(7), 1175–1187 (2021)
Li, T., Chen, L., Jensen, C.S., Pedersen, T.B., Gao, Y., Hu, J.: Evolutionary clustering of moving objects. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 2399–2411. IEEE (2022)
Li, T., Huang, R., Chen, L., Jensen, C.S., Pedersen, T.B.: Compression of uncertain trajectories in road networks. Proc. VLDB Endowment 13(7), 1050–1063 (2020)
Moutafis, P., García-García, F., Mavrommatis, G., Vassilakopoulos, M., Corral, A., Iribarne, L.: Algorithms for processing the group k nearest-neighbor query on distributed frameworks. Distrib. Parallel Databases 39, 733–784 (2021)
Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group nearest neighbor queries. In: Proceedings. 20th International Conference on Data Engineering, pp. 301–312. IEEE (2004)
Xu, H., Li, Z., Lu, Y., Deng, K., Zhou, X.: Group visible nearest neighbor queries in spatial databases. In: Chen, L., Tang, C., Yang, J., Gao, Y. (eds.) Web-Age Information Management, pp. 333–344. Springer, Berlin Heidelberg, Berlin, Heidelberg (2010)
Yang, D., Shastri, A., Rundensteiner, E.A., Ward, M.O.: An optimal strategy for monitoring top-k queries in streaming windows. In: Proceedings of the 14th International Conference on Extending Database Technology, pp. 57–68 (2011)
Yiying, J., Liping, Z., Feihu, J., Xiaohong, H.: Groups nearest neighbor query of mixed data in spatial database. J. Front. Comput. Sci. Technol. 16(2), 348 (2022)
Zhu, R., Wang, B., Luo, S.Y., Yang, X.C., Wang, G.R.: Approximate continuous top-k query over sliding window. J. Comput. Sci. Technol. 32(1), 93–109 (2017)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-46677-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46676-2
Online ISBN: 978-3-031-46677-9
eBook Packages: Computer ScienceComputer Science (R0)