文章基本信息

标题：Column-Store: Decision Tree Classification of Unseen Attribute Set
本地全文：下载
作者：Tejaswini Apte ; Maya Ingle ; A.K. Goyal 等
期刊名称：International Journal of Database Management Systems
印刷版ISSN：0975-5985
电子版ISSN：0975-5705
出版年度：2013
卷号：5
期号：6
DOI：10.5121/ijdms.2013.560335
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：A decision tree can be used for clustering of frequently used attributes to improve tuple reconstruction time in column-stores databases. Due to ad-hoc nature of queries, strongly correlative attributes are grouped together using a decision tree to share a common minimum support probability distribution. At the same time in order to predict the cluster for unseen attribute set, the decision tree may work as a classifier. In this paper we propose classification and clustering of unseen attribute set using decision tree to improve tuple reconstruction time