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一种障碍空间数据库中的连续反k近邻查询方法 被引量：6

Method for Continuous Reverse k-Nearest Neighbor Queries in Obstructed Spatial Databases
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摘要 随着智能移动设备和无线定位技术的飞速发展,使用基于位置服务应用的用户越来越多.特别地,不同于传统的针对固定位置的快照查询,移动的用户往往基于移动轨迹发出连续的查询.在真实和虚拟的空间环境中,障碍物的影响都是广泛存在的,障碍空间内的查询处理技术得到了越来越多的关注,其中,障碍空间内的连续反k近邻查询处理有着重要的应用.对障碍空间中的连续反k近邻查询问题进行了定义和系统的研究,通过定义控制点和分割点,提出了针对该问题的处理框架.进一步地,提出了一系列的过滤和求精算法,包括剪枝数据集、获取障碍物、剪枝和计算控制点和更新结果集等处理策略.基于多种数据集对所提出的算法进行了实验评估.与针对每个数据点进行k近邻计算的基本方法相比,这些方法可以大幅度提高查询处理的CPU和I/O效率. With the rapid development of smart mobile devices and wireless location techniques, more and more users tend to attempt location-based service. Specifically, mobile users usually request continuous queries based on moving trajectories instead of traditional snap-shot queries for fixed locations. As obstacles can be found everywhere in the real-world or virtual space, more and more attentions has been paid on query processing techniques in the obstructed space. Notably, continuous reversek-nearest neighbor queries in obstructed space are widely used. This paper presents an in-depth study on the problem of moving reversek-nearest neighbor queries in obstructed spatial databases. By defining control points and split points, the processing framework for this problem is constructed. Furthermore, several pruning and verification algorithms, including data points reduction, obstacles retrieving, control points calculating and results set updating, are proposed to improve the query efficiency. Extensive experimental evaluation is conducted based on various datasets. Compared with the basic method which computes thek-nearest neighbors for each data point, the proposed methods can significantly improve CPU and I/O efficiency.
作者 谷峪 于晓楠 于戈
机构地区 东北大学信息科学与工程学院 医学影像计算教育部重点实验室(东北大学)
出处 《软件学报》 EI CSCD 北大核心 2014年第8期1806-1816,共11页 Journal of Software
基金 国家重点基础研究发展计划(973)(2012CB316201) 国家自然科学基金(61003058 61033007 61202086) 中央高校基本科研业务费专项资金(N130404010)
关键词 连续查询 反k近邻 障碍空间 查询优化 控制点 continous query reversek-nearest neighbor obstructed space query optimization control point
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献18

  • 1Zhang J, Zbu ML, Papadias D, Tao YF, Lee DL. Location-Based spatial queries. In: Proc. of the 2003 ACM SIGMOD Int'l Conf. on Management of Data. San Diego: ACM Press, 2003. 443-454. [doi:10.1145/872757.872812].
  • 2Roussopoulos N, Kelley S, Vincent F, Nearest neighbor queries. In: Proc. of the '95 ACM SIGMOD Int'l Conf. on Management of Data. San Jose: ACM Press, 1995.71-79. Idol: 10.1145/223784.223794].
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  • 4Gao YJ, Zheng BH, Chen GC, Li Q, Guo XF. Continuous visible nearest neighbor query processing in spatial databases. VLDB Journal, 2011,20(3):371-396. [doi: 10.1007/s00778-010-0200-z].
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  • 7Stanoi 1, Agrawal D, Abbadi AE. Reverse nearest neighbor queries for dynamic databases. In: Proc. of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. 2000.44-53.
  • 8Tao YF, Papadias D, Lian X. Reverse kNN search in arbitrary dimensionality. In: Proc. of the 30th Int'l Conf. on Very Large Data Bases. Toronto: Morgan Kaufmann Publishers, 2004. 744-755.
  • 9Wu W, Yang F, Chan CY, Tan KL. Finch: Evaluating reverse k-nearest-neighbor queries on location data. Proc. of the VLDB Endowment, 2008,1 ( 1 ): 1056-1067.
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二级参考文献29

  • 1Zhang J, Zhu M, Papadias D, Tao Y. Location-based spatial queries//Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data. New York, USA, 2003:443-454.
  • 2Roussopoulos N, Kelley S, Vincent F. Nearest neighbor queries//Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data. New York, USA, 1995: 71- 79.
  • 3Gao Y, Zheng B, Chen Get al. Continuous visible nearest neighbor query processing in spatial databases. VLDB Journal, 2010, 20(3): 371- 396.
  • 4Sharifzadeh M, Shahaloi C. The spatial skyline queries//Proceedings of the VLDB. Seoul, Korea, 2006:751-762.
  • 5Lee K C, Lee W C, Zheng B et al. Z-SKY: An efficient skyline query processing framework based on Z-order. VLDB Journal, 2010, 19(3): 333-362.
  • 6Stanoi I, Agrawal D, Abbadi A E. Reverse nearest neighbor queries for dynamic databases//Proceedings of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. 2000: 44-53.
  • 7Tao Y, Papadias D, Lian X. Reverse kNN search in arbitrary dimensionality//Proceedings of the VLDB. Toronto, Canada, 2004:744-755.
  • 8Sharifzadeh M, Shahabi C. VoR-Tree: R-trees with voronoi diagrams for efficient proeessing//Proceedings of the VLDB. Singapore, 2010, 1231-1242.
  • 9Wu W, Yang F, Chan C Yet al. Finch: Evaluating reverse k-nearest-neighbor queries on location data//Proceedings of the VLDB. Auckland, New Zealand, 2008:1056-1067.
  • 10Achtert E, Bohm C, Kroger Pet al. Efficient reverse k-nearest neighbor estimation. Informatik Forschung und Entwicklung, 2007, 21(3-4): 179-195.

共引文献23

同被引文献40

  • 1朱良,孙未未,荆一楠,杜江帆.基于Voronoi图的路网k聚集最近邻居节点查询方法[J].计算机研究与发展,2011,48(S3):155-162. 被引量：5
  • 2Korn F, Muthukrishnan S. Influence Sets Based on Reverse Nearest Neighbor Queries [J]. ACM Sigmod Record, 2000, 29(2): 201-212.
  • 3ZHAO Geng, XUAN Kefeng, Taniar D. Path kNN Query Processing in Mobile Systems [J]. IEEE Transactions on Industrial Electronics, 2013, ,60(3) : 1099-1107.
  • 4WU Wei, YANG Fei, Chan C Y, et al. Continuous Reverse k-Nearest-Neighbor Monitoring International Conference on Mobile Data Management. Piscataway, NJ: IEEE, 2008: 132-139.
  • 5Cheema M A, ZHANG Wenjie, LIN Xuemin, et al. Continuous Reverse k Nearest Neighbors Euclidean Space and in Spatial Networks [J]. VLDB, 2012, 21(1): 69-95.
  • 6[ C]//9th Queries in Cho H J, Kwon S J, Chung T S. A Safe Exit Algorithm for Continuous Nearest Neighbor Monitoring in Road Networks [J]. Mobile Information Systems, 2013, 9(1): 37 53.
  • 7YANG Shiyu, Cheema M A, LIN Xuemin. SLICE: Reviving Regions-Based Pruning for Reverse k Nearest Neighbors Queries. Data Engineering (ICDE) [C]//2014 IEEE 30th International Conference on Data Engineering. Piscataway, NJ: IEEE, 2014: 760-771.
  • 8LU Ying, LU Jiaheng, CONG Gao, et al. Efficient Algorithms and Cost Models for Reverse Spatial-Keyword k Nearest Neighbor Search [J/OL]. ACM Transactions on Database Systems, 20140501, doi: 10. 1145/2576232.
  • 9GAO Yunjun, YANG Jiacheng, CHEN Gang, et al. On Efficient Obstructed Reverse Nearest Neighbor Query Processing [C]//Proceedings of the 19tb ACM Sigspatial International Conference on Advances in Geographic Information Systems. New York: ACM, 2011: 191-200.
  • 10TAO Yufei, Papadias D, LIAN Xiang. Reverse kNN Search in Arbitrary Dimensionality EC~//Proceedings of the Thirtieth International Conference on Very Large Data Bases. New York: ACM, 2004: 744-755.

引证文献6

二级引证文献14

软件学报
2014年 第8期

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