期刊文献+

基于模拟退火算法的空间度量物化选择 被引量:4

Selective materialization of special measures based on simulated annealing algorithm
下载PDF
导出
摘要 为了解决空间OLAP的响应速度存在着存储空间和时间代价的矛盾,通过空间立方体的选择物化方法来实现空间要素有效而实用的选择合并,提高联机分析的响应速度.采用模拟退火算法,以空间对象面状区域的合并为例,进行空间度量物化选择,将模拟退火算法融入PIA算法中,同时把空间对象(面状)与其它类型的空间对象(点状、线状)的关联关系(交、含、邻)作为空间对象合并的共享性与实用性的考查指标,加入目标函数当中.实验结果表明:随着空间对象数据的增加,模拟退火算法与PIA算法,两种算法的时间代价仅有较少的增长,均具较好的伸缩性,在空间对象数目100-400时,PIA算法优于模拟退火算法,当空间对象数目大于400后模拟退火算法时间代价缓慢增长,而PIA算法时间代价急剧增大;在模拟退火算法中空间对象集合的空间关联度越高,选中几率越高.融入PIA的模拟退火算法具有良好的伸缩性,并提高了空间度量合并解的优化,增加了空间度量选择物化的实用性. In order to solve the contradiction between storage space and storage time of spatial OLAP response speed, the selective materialization method of spatial cubes was developed. This method adopts the simulated annealing algorithm and takes the area regional merger of spatial objects as example to achieve the materialized selection of spatial measure. The simulated annealing algorithm was introduced into the pointer intersection algorithm(PIA), and the associated relation(intersect, include and adjacent) of spatial objects (area) and other types of space objects (point, line) was taken as test indicators for the characters of the common sharing and practicality of combining spatial objects, which joined the objective function. The results show that: with the data of spatial objects increased, the time cost of simulated annealing algorithm and PIA increases a little and both of them perform a better scalability. When the number of spatial objects is from 100 to 400, PIA is superior to the simulated annealing algorithm; when it is over 400, the time cost of simulated annealing algorithm grows slowly, while the time cost of PIA increases rapidly. The higher is the degree of spatial correlation of spatial objects setting in the simulated annealing algorithm, the higher is the probability of being selected. The simulated annealing algorithm combining with the pointer intersection algorithm optimizes the merge solution of spatial measurement, and enhances the practicability of the selective materialization of spatial measurement.
出处 《哈尔滨工业大学学报》 EI CAS CSCD 北大核心 2008年第7期1099-1102,共4页 Journal of Harbin Institute of Technology
基金 国家自然科学基金资助项目(40771154) 黑龙江省自然科学基金资助项目(TK2005-17) 黑龙江省高校教师骨干计划资助项目(151G021) 哈尔滨师范大学教师骨干计划资助项目
关键词 模拟退火算法 空间OLAP 空间数据立方体 空间数据挖掘 simulated annealing algorithm spatial online analytical processing (OLAP) spatial data cube spatial data mining
  • 相关文献

参考文献8

  • 1CLEMENTINI E, FELICE P D, KOPERSKI K. Mining multiple-level spatial association rules for obje. is with a broad boundary [ J ]. Data & Knowledge Engineering, 2000, 34 (3) :251 - 270.
  • 2KOPERSKI K, ADHIKARY J, HAN J. Spatial data mining: progress and challenges [ C ]//Proc SIGMOD' 96 Workshop on Research Issues on Data Mining and Knowledge Discovery, DMKD'96, Montreal, Canada: [s.n. ], 1996:1 -10.
  • 3CHAWLA S, SHEKHAR S, WU W L, et al. Modeling spatial dependencies for mining geospatial data: an introduction. [ M ]//MILLER H, HAN Jiawei. Geographic Data Mining and Knowledge Discovery (GKD). London and New York : [ s. n. ] , 2001 : 139 - 142.
  • 4XIONG Fanlun, HUAI Xiaoyong, YUAN Hongchun. Research on new generation intelligent information systems tool [ C]//International Symposium on Intelligent Agriculture Information Technology (ISIAIT2000). Beijing: [s. n. ], 2000:61 -66.
  • 5HAN Jiawei, KAMBER M. Data mining concepts and techniques [ M]. Beijing: Higher Education Press, 2001.
  • 6YUAN Hongehun, XIONG Fanlun. Study on model of spatial data mining based on double-bases cooperating mechanism [ C ]//Proc 4^th World Congress on Intelligent Control and Automation. Shanghai : [ s. n. ], 2002 : 1489 - 1493.
  • 7HAN Jiawei, STEFANOVIC N. Selective materialization: a efficient method for spatial data cube construction [ C ]// Proc 1998 Pacific-asia Conference on Konowledge Dis- covery and Data Mining ( PAKDD ' 98 ). Australia: Springer, 1998 : 144 - 158.
  • 8STEFANOVIC N, HAN Jiawei, KOPERSKI K. Object- based selective materialization for efficient implementation of spatial data cubes [ J ]. IEEE Transactions on Knowledge and Data Engineering, 2000,12 (6) : 938 - 958.

同被引文献28

  • 1南金瑞,王建群,孙逢春.电动汽车能量管理系统的研究[J].北京理工大学学报,2005,25(5):384-389. 被引量:17
  • 2孙丕忠,夏智勋,黄琳.基于遗传算法的多级固体火箭总体/发动机一体化设计优化研究[J].宇航学报,2005,26(B10):1-4. 被引量:8
  • 3刘岩,韩承德,王义和,李晓明.模拟退火算法的背景与单调升温的模拟退火算法[J].计算机研究与发展,1996,33(1):4-10. 被引量:20
  • 4樊博,李海刚,孟庆国.空间数据立方体的建模方法研究[J].计算机工程,2007,33(8):1-2. 被引量:9
  • 5Yu Songmei, Vijayalakshmi A, Nabil A. Selective View Materialization in a Spatial Data Warehouse[C]//Proc. of the 7th International Conference on Data Warehousing and Knowledge Discovery. Copenhagen, Danmark: [s. n.], 2005:157-167.
  • 6Stefanovic N, Han Jiawei, Koperski K. Object-based Selective Materialization for Efficient Implementation of Spatial Data Cube[J]. 1EEE Trans. on Knowledge and Data Engineering, 2000, 12(6): 938-958.
  • 7Korel B. Dynamic method for software test data generation [ J]. Journal of Software: Testing, Verification and Reliabili- ty, 1992,2(4) :203 - 213.
  • 8Bertsimas D,Tsitsiklis J. Simulated annealing[ J]. Statistical Science, 1993,8( 1 ) : 10 - 15.
  • 9Ghiduk A S, Harrold M J, Girgis M R. Using genetic algo- rithms to aid test-data generation for data-flow coverage [ C]//14^th Asia-Pacific Software Engineering Conference. 2007:41 - 48.
  • 10陈全世.先进电动汽车技术[M],北京:化学工业出版社,2013.

引证文献4

二级引证文献17

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