摘要
现有的静态物化视图选择算法的视图搜索代价较大 ,而导致算法的时间复杂度偏高 ,不能用于对物化视图进行在线动态调整 提出了一种物化视图选择的预处理算法———PMVS ,其中包括用户查询集动态调整算法QSDM、候选视图格构造算法CVLC和候选视图筛选算法CVF ,该算法可用做预处理过程对视图数量进行在线压缩 ,从而降低了静态算法的视图空间搜索代价和时间复杂度
The availability and performance of data warehouse is gradually degrading with the altering requirements One of the biggest issues is that the set of materialized views is far from the optimal, so it is necessary for implementing the dynamic adjustment to match the demand of the users Since the current static algorithms are not suitable for this purpose on account of their larger space search and higher time consumption, this paper presents PMVS (preprocessor of materialized views selection), an approach composed of three algorithms: QSDM (query set dynamic management), CVLC (candidate view lattice construction) and CVF (candidate view filter) Of all these three algorithms, QSDM monitors the distribution of each query and determines by hypothesis test whether the query should be added into or discarded from the query set And based on the given query set, CVLC is in charge of producing candidate view set, which is proven to be sufficient and necessary for selecting the best set of materialized views As a heuristic algorithm, CVF then utilizes the character of data sparse in multi dimensional datasets to remove a part of candidate views that offer very limited contribution to the optimal solution The comparative experiment indicates that PMVS can be employed by the static algorithms to reduce effectively the amount of views beforehand, and the cost of static algorithms on space and time can be cut down to fit for online demand
出处
《计算机研究与发展》
EI
CSCD
北大核心
2004年第10期1645-1651,共7页
Journal of Computer Research and Development
基金
国家自然科学基金项目 ( 70 3 710 15 )
关键词
物化视图
预处理算法
多维数据集
数据仓库
materialized view
preprocessor
multi dimensional dataset
data warehouse
