期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

高效支持多维网络OLAP的数据立方体模型CI-DCG

CI-DCG:an efficient data cube model for supporting OLAP on multidimensional networks
下载PDF
导出
摘要 针对现有联机分析处理(OLAP)方法的空间开销随着数据维度增加呈指数级增长,因而不适用于维度较高的多维网络应用的问题,提出了一种新的多维网络数据立方体模型——封闭冰山双立方图(CI-DCG)。该模型通过引入邻接立方体的概念,将其实例化过程转化为两个计算传统数据立方体的阶段,从而可将传统数据立方体生成算法中较为成熟的空间优化技术引入到多维网络中。在保证多维网络上OLAP查询处理效率的同时,将多维网络数据立方体生成算法的空间复杂度降为多项式级别。理论分析和实验结果均表明,该模型在空间开销和查询性能方面均优于已有的多维网络OLAP模型,并且数据维度越高,这种优势就越明显。 Considering that it is valuable to support efficient on-line analytical process（OLAP） query on multidimensional networks and the space overhead of the existing OLAP methods grows exponentially with the increase of the data di- mensionality, which limits their use in multidimensional networks with high dimensionality, the closed iceberg double cubed graph （CI-DCG） , a novel data cube model is proposed. By introducing the concept of adjacent cube, the mod- el splits the process of materializing into two phases of data cube computing, which facilitates combining special characteristics of multidimensional networks with the existing well-studied data cube techniques, to gain high query performance with polynomialspace complexity. Both theoretical analysis and experimental results demonstrate the ef- ficiency and effectiveness of the CI-DCG,especially in case of high dimensionality.
作者 古晓艳 王伟平 孟丹 杨秀峰 周江
机构地区 中国科学院计算技术研究所计算应用研究中心 中国科学院研究生院 中国科学院信息工程研究所
出处 《高技术通讯》 CAS CSCD 北大核心 2013年第10期1030-1037,共8页 Chinese High Technology Letters
基金 863计划(2011AA01A203 2012AA011002) 国家自然科学基金(60903047) 中国科学院先导专项(XDA06030200)资助项目
关键词 多维网络 图立方体 邻接立方体 联机分析处理(OLAP) multidimensional network, graph cube, adjacency cube, on-line analytical process（OLAP）
分类号 TP311.13 [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21

  • 1Shi J, Malik J. Normalized cuts and image segmentation. 1EEE Trans on Pattern Analysis and Machine Intelligence, 2000,22( 8 ) :888-905.
  • 2Newman M E J, Girvan M. Finding and evaluating commu- nity structure in networks. Physical Review E, 2004,69 (2) :026113.
  • 3Gibson D, Kumar R, Tomkins A. Discovering large dense snbgraphs in massive graphs. In:Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, 2005.721-732.
  • 4Xu X,Yuru k N, Feng Z, et al. Scan: a structural cluste- ring algorithm for networks. In: Proceedings of the 13th ACM Special Interest Group on Knowledge Discovery and Data Mining, San Jose, USA, 2007. 824-833.
  • 5Navlakha S, Rastogi R, Shrivastava N. Graph summariza- tion with bounded error. In : Proceedings of the 2008 ACM Special Interest Group on Management of Data, Vancou- ver, Canada, 2008.419 -432.
  • 6LeFevre K, Terzi E. GraSS:Graph structure summariza- tion. In:Proceedings of 2010 International Conference on Society for Industrial and Applied Mathematics, Ohio, USA ,2010. 454465.
  • 7Zhao P, Li X, Xin D, et al. Graph cube:on warehousing and olap multidimensional networks. In:Proceedings of the 2011 ACM Special Interest Group on Management of Da- ta, Athens, Greece ,2011. 853-864.
  • 8Tian Y, Hankins R A, Patel J M. Efficient aggregation for graph summarization. In: Proceedings of the 2008 ACM Special Interest Group on Management of Data, Vancou- ver, BC, Canada, 2008. 567-580.
  • 9Zhang N ,Tian Y, Pate1 J M. Discovery-driven graph sum- marization. In: Proceedings of IEEE 26th International Conference on Data Engineering, Long Beach, USA ,2010. 880-891.
  • 10Zhou Y, Cheng H, Yu J X. Graph clustering based on structural/attribute similarities. In: Proceedings of the Very Large Data Base Endowment, Lyon, France, 2009. 718-729.

二级参考文献13

  • 1Lakshmanan LVS, Pei J, Han JW. Quotient cube: How to summarize the semantics of a data cube. In: Bressan S, Chaudhri AB, Lee ML, Yu JX, Lacroix Z, eds. Proc. of the 23rd Int'l Conf. on Very Large Data Bases. Hong Kong: Morgan Kaufmann, 2002. 778～789.
  • 2Sismanis Y, Deligiannakis A, Roussopoulos N, Kotidis Y. Dwarf: Shrinking the PetaCube. In: Franklin MJ, Moon B, Ailamaki A, eds. Proc. of the 2002 ACM SIGMOD Int'l Conf. on Management of Data. Madison: ACM Press, 2002. 464～475.
  • 3Mumick IS, Quass D, Mumick BS. Maintenance of data cubes and summary tables in a warehouse. In: Peckham J, ed. Proc. of the ACM SIGMOD Int'l Conf. on Management of Data. Tucson: ACM Press, 1997. 100-111.
  • 4Hahn C, Warren S, London J. Edited synoptic cloud reports from ships and land stations over the globe. 1996. http://cdiac.esd.ornl.gov/cdiac/ndps/ndp026b.html
  • 5Gray J, Bosworth A, Layman A, Pirahesh H. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. In: Su SYW, ed. Proc. of the 12th Int'l Conf. on Data Engineering. New Orleans: IEEE Computer Society, 1996. 152～159.
  • 6Agarwal S, Agrawal R, Deshpande PM, Gupta A, Naughton JF, Ramarkrishman R, Sarawagi S. On the computation of multidimensional aggregates. In: Vijayaraman TM, Buchmann AP, Mohan C, Sarda NL, eds. Proc. of the 22nd Int'l Conf. on Very Large Data Bases. Mumb
  • 7Zhao Y, Deshpande PM, Naughton JF. An array-based algorithm for simultaneous multidimensional. In: Peckham J, ed. Proc. of the ACM SIGMOD Int'l Conf. on Management of Data. Tucson: ACM Press, 1997. 159-170.
  • 8Ross KA, Srivastava D. Fast computation of sparse datacubes. In: Jarke M, Carey MJ, Dittrich KR, Lochovsky FH, Loucopoulos P, Jeusfeld MA, eds. Proc. of the 23rd Int'l Conf. on Very Large Data Bases. Athens: Morgan Kaufmann, 1997. 116～125.
  • 9Harinarayan V, Rajaraman A, Ullman JD. Implementing data cubes efficiently. In: Jagadish HV, Mumick IS, eds. Proc. of the 1996 ACM SIGMOD Int'l Conf. on Management of Data. Montreal: ACM Press, 1996. 205-216.
  • 10Shukla A, Deshpande PM, Naughton JF. Materialized view selection for multidimensional datasets. In: Gupta A, Shmueli O, Widom J, eds. Proc. of the 24th Int'l Conf. on Very Large Data Base. New York: Morgan Kaufmann, 1998. 488～499.

共引文献24

高技术通讯
2013年 第10期

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