• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Intelligent Information Systems >Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering
【24h】

Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering

机译：通过分层聚类有效，高效地支持连续维度上的上滚和下钻OLAP操作

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In traditional OLAP systems, roll-up and drill-down operations over data cubes exploit fixed hierarchies defined on discrete attributes, which play the roles of dimensions, and operate along them. New emerging application scenarios, such as sensor networks, have stimulated research on OLAP systems, where even continuous attributes are considered as dimensions of analysis, and hierarchies are defined over continuous domains. The goal is to avoid the prior definition of an ad-hoc discretization hierarchy along each OLAP dimension. Following this research trend, in this paper we propose a novel method, founded on a density-based hierarchical clustering algorithm, to support roll-up and drill-down operations over OLAP data cubes with continuous dimensions. The method hierarchically clusters dimension instances by also taking fact-table measures into account. Thus, we enhance the clustering effect with respect to the possible analysis. Experiments on two well-known multidimensional datasets clearly show the advantages of the proposed solution.
机译：在传统的OLAP系统中，对数据多维数据集的上滚和下钻操作会利用在离散属性上定义的固定层次结构，这些层次结构扮演维的角色并沿维进行操作。诸如传感器网络之类的新兴应用场景刺激了对OLAP系统的研究，在该系统中，甚至连连续的属性都被视为分析的维度，并且在连续的域上定义了层次结构。目的是避免沿每个OLAP维度预先定义临时离散化层次结构。遵循这一研究趋势，在本文中，我们提出了一种基于基于密度的层次聚类算法的新颖方法，以支持具有连续维的OLAP数据多维数据集上的上滚和下钻操作。该方法还通过考虑事实表度量，对维度实例进行分层聚类。因此，相对于可能的分析，我们增强了聚类效果。在两个著名的多维数据集上进行的实验清楚地表明了该解决方案的优势。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号