Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (27): 162-164.DOI: 10.3778/j.issn.1002-8331.2008.27.052

• 数据库、信号与信息处理 • Previous Articles     Next Articles

Research of cluster based on feature vectors in autonomic database

QIANG Yan,CHEN Jun-jie,GAO Yan-fei   

  1. College of Computer Engineering and Software,Taiyuan University of Technology,Taiyuan 030024,China
  • Received:2007-11-08 Revised:2008-02-27 Online:2008-09-21 Published:2008-09-21
  • Contact: QIANG Yan

自适应数据库中基于特征向量的聚类算法

强 彦,陈俊杰,高燕飞   

  1. 太原理工大学 计算机与软件学院,太原 030024
  • 通讯作者: 强 彦

Abstract: In autonomic database system,workload characterization is a key part.In workload characterization,workload should be classified,then anticipate workload performance.Workload classification uses cluster algorithm.And in cluster algorithm,the typical is the K-means cluster algorithm.But in the K-means cluster algorithm,k should be defined and not changed.This paper makes an improvement in the algorithm,so the k is changed if needed.The result of the experiment shows that the veracity using Cluster algorithm Based on Feature Vectors(CFV) making of forecasting workload runtime is improved.

摘要: 负载自适应数据库系统中,负载特征化部件要实时对各种数据库的访问负载分类,根据分类的情况预测负载对数据库资源需求。是对常规聚类算法的一个改进,提出基于特征向量的聚类算法和基于特征向量的增量聚类算法。使用该算法后负载分类速度和准确性有明显提高。