期刊文献+

MapReduce框架下基于R-树的K-近邻连接算法设计

下载PDF
导出
摘要 计算机技术的发展,使得算法的统计被逐步的处理出来,大规模的数据处理必须被目前阶段的算法所满足,这使得Map Reduce框架下基于R-树的K-近邻连接算法被逐步应用。在Map Reduce框架下,通过抽象提取的方式,能够有效地使得R-树的算法能够很好地被K-的临近算法所使用。本文就主要对Map Reduce框架下基于R-树的K-近邻连接算法的设计进行了分析。
出处 《数字技术与应用》 2015年第7期135-135,共1页 Digital Technology & Application
  • 相关文献

参考文献2

二级参考文献12

  • 1倪巍伟,陆介平,孙志挥.基于向量内积不等式的分布式k均值聚类算法[J].计算机研究与发展,2005,42(9):1493-1497. 被引量:15
  • 2Bohm C, Krebs F. The k-nearest neighbor join: Turbo charging the KDD process. Knowledge Information System, 2004,6(6): 728-749. [doi: 10.1007/s10115-003-0122-9].
  • 3Xia CY, Lu HJ, Coi BC, Hu J. Gorder: An efficient method for KDD joins processing. In: Proc. of the 30th Int'l Conf. on Very Large Data Bases (VLDB). 2004. 756-767.
  • 4Yao B, Li FF, Kumar P. K nearest neighbor queries and KNN-joins in large relational databases (almost) for free. In: Proc. of the 26th Int'l Conf. on Data Engineering (ICDE). 2010.4-15. [doi: 10.1109/ICDE.2010.5447837].
  • 5Yu C, Cui B, Wang SG, Su JW. Efficient index-based KNN join processing for high-dimensional data. Information and Software Technology, 2007,49(4):332-344. [doi: 10.1016/j.infsof.2006.05.006].
  • 6Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 2008,51(1):107-113 [doi: 10.1145/1327452.1327492].
  • 7White T. Hadoop: The Definitive Guide. Sebastopol: Yahoo! Press, 2009.
  • 8Zhang C, Li FF, Jestes J. Efficient parallel kNN joins for large data in MapReduce. In: Proc. of the 15th Int'l Conf. on Extending Database Technology (EDBT). 2012.38-49. [doi: 10.1145/2247596.2247602].
  • 9Lu W, Shen YY, Chen S, Col BC. Efficient processing of k nearest neighbor joins using MapReduce. In: Proc. of the 38th lnt'l Conf. on Very Large Data Bases (VLDB). 2012. 1016-1027.
  • 10Liu Y, Jing N, Chen L, Chen HZ. Parallel bulk-loading of spatial data with MapReduce: An R4ree case. Wuhan University Journal of Natural Sciences, 2011,16(6):513-519. [doi: 10.1007/s11859-011-0790-3].

共引文献107

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