期刊文献+

基于道路网的连续k近邻查询算法 被引量:3

Continuous k nearest neighbor query algorithm based on road network
下载PDF
导出
摘要 针对增量式监测算法(IMA)的冗余搜索问题,提出一种基于IMA改进的移动对象连续k近邻(Continuous k Nearest Neighbor,CkNN)查询处理新算法。采用增量式查询处理机制;利用距离相近的查询其查询结果大部分相同这一特性,在以查询点为中心进行网络扩展之前,首先执行一个预处理过程,分析相近的其他查询的扩展树,并重用其中的有效部分,从而避免了对道路网的盲目扩展;且在节点的网络扩展中,通过应用具有相同扩展方向的其他查询的扩展结果,不仅减少了对道路网的重复扩展,还节省了计算代价。实验结果表明,所提算法同传统算法相比较,缩短了查询响应时间,提高了运行效率,并且适用于不同类型的k近邻查询。 Concerning the problem of redundant search of Incremental Monitoring Algorithm (IMA), this paper proposed a new algorithm of improving Continuous k Nearest Neighbor (Ck NN) queries for moving objects based on IMA. The incremental query processing mechanism was adopted. Adopting the characteristic that the close queries have similar results, a pretreatment process was first performed before the network expansion which took query point as the center, by investigating the expansion tree of other queries and reuse the effective part, thus avoiding blind expansion of road network. During the network expansion of nodes, by applying the expansion results of other queries which have the same expansion direction, not only repetitive expansion was reduced, but also computational cost was saved. The experimental results show that the query response time of proposed algorithm is reduced, the efficiency is improved compared with the traditional algorithm. In addition, the improved algorithm is applicable to different types of k nearest neighbor queries.
出处 《计算机应用》 CSCD 北大核心 2013年第7期1964-1968,共5页 journal of Computer Applications
基金 国家自然科学基金资助项目(61073023)
关键词 增量式监测算法 移动对象 连续K近邻查询 网络扩展 扩展树 道路网 Incremental Monitoring Algorithm (IMA) moving object Continuous k Nearest Neighbor (CkNN) query network expansion expansion tree road network
  • 相关文献

参考文献13

  • 1KOLAHDOUZAN M, SHAHABI C. Voronoi-based k nearest neigh- bor search for spatial network databases[ C]// Proceedings of the 30th Very Large Data Base Conference. Toronto: VLDB Endow- ment, 2004:840-851.
  • 2王淼,郝忠孝.基于动态创建局部Voronoi图的连续近邻查询[J].计算机应用研究,2008,25(9):2771-2774. 被引量:4
  • 3HUANG X G, JENSEN C S, SALTENIS S. The islands approach to nearest neighbor querying in spatial networks[ C]// Proceedings of the 9th International Symposium on Spatial and Temporal Databases. Berlin: Springer-Verlag, 2005:73-90.
  • 4XIONG X P, MOKBEL F M, AREF W G. SEA-CNN: scalable pro- cessing of continuous k -nearest neighbor queries in spatio-temporal databases[ C]// ICDE 2005: Proceedings of the 21st International Conference on Data Engineering. Piscataway: IEEE, 2005:643 - 654.
  • 5YU X H, PU K Q, KOUDAS N. Monitoring k-nearest neighbor que- ries over moving objects [ C]//ICDE 2005: Proceedings of 21st In- ternational Conference on Data Engineering. Piscataway: IEEE, 2005: 631 - 642.
  • 6MOURATIDIS K, HADJIELEFrI-IERIOU M. Conceptual partitio- ning: an efficient method for continuous nearest neighbor monitoring [C]// Proceedings of ACM SIGMOD 2005. New York: ACM, 2005:634-645.
  • 7卢秉亮,刘娜.路网中移动对象快照K近邻查询处理[J].计算机应用,2011,31(11):3078-3083. 被引量:4
  • 8梁茹冰,刘琼.公路网移动终端的KNN查询技术[J].华南理工大学学报(自然科学版),2012,40(1):138-145. 被引量:2
  • 9赵亮,陈荦,景宁,廖巍.道路网中的移动对象连续K近邻查询[J].计算机学报,2010,33(8):1396-1404. 被引量:14
  • 10MOURATIDIS K, YIU M L, PAPADIAS D, et al. Continuous nea- rest neighbor monitoring in road networks[ C] //Proceedings of the 32nd International Conference on Very Large Data Bases. Toronto: VLDB Endowment, 2006:43-54.

二级参考文献62

  • 1TAO YU-FEI, PAPADIAS D. Spatial queries in dynamic enviroment[ J]. ACM Transactions on Database Systems, 2003, 28(2) : 101 - 139.
  • 2SALTENIS D, JENSEN C S. Indexing the positions of continuously moving objects[ J]. ACM SIGMOD Record, 2000, 29 (2) : 331 - 342.
  • 3BENETIS R, JENSEN C S, KARCIAUSKAS G, et al . Nearest neighbor and reverse nearest neighbor queries for moving objects [ EB/OL]. [ 2008 - 11 -20] . http://www, cs. aau. dk/- tbp/ Teaching/DAT5 EO 1/benetis. pdf.
  • 4TAO YU-FEI, PAPADIAS D. Time-parameterized queries in spatiotemporal databases[ C]// Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2002:334 - 345.
  • 5IWERKS G S, SAMET H, SMITH K. Continuous k-nearest neighbor queries for continuously moving points with updates[ C]//Proceedings of the 29th International Conference on Very large Data Bases. Berlin, Germany: VLDB Endowment, 2003:512-523.
  • 6MOKBEL M F, XIONG XIAOPENG, AREF W G. SINA: Scalable incremental processing of continuousqueries in spatiotemporal databases[ EB/OL]. [ 2008 - 11 -20]. http://www, cs. purdue, edu/ homes/mokbel/SINA-SIGMOD04, pdf.
  • 7HU HAIBO, XU JIANLIANG, LEE D L. A generic framework for monitoring continuous spatial queries over moving objects[ C]//Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, New York: ACM, 2005:479 -490.
  • 8XIONG XIAOPENG, MOKBEL M F, AREF W G. SEA - CNN : Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases[ EB/OL]. [ 2008 - 11 - 20]. http://www - users, es. umn. edu/- mokbel/papers/icde05, pdf.
  • 9YU XIAOHUI, PU K Q, KOUDAS N. Mointoring k-nearest neighbour queries over moving objects[ C]// Proceedings of the 21st International Conference on Data Engineering. Washington, DC: IEEE Computer Society, 2005:631 - 642.
  • 10MOURATIDIS K, HADJIELEFTHERIOU M, PAPADIAS D. Conceptual Partitioning: An efficient method for continuous nearest neighbor monitofirtg[ C] //Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2005:634 -645.

共引文献21

同被引文献16

引证文献3

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部