AN EFFICIENT APPROACH FOR VIEW SELECTION FOR DATA WAREHOUSE USING TREE MINING AND EVOLUTIONARY COMPUTATION

被引：0

作者：

Thakare, Atul ^{[1
]}

Deshpande, Parag ^{[1
]}

机构：

[1] Visvesvaraya Natl Inst Technol, Comp Sci & Engn Dept, South Ambazari Rd, Nagpur 440010, Maharashtra, India

来源：

COMPUTER SCIENCE-AGH | 2018年 / 19卷 / 04期

关键词：

database management systems; data warehousing and data mining; query optimization; graph mining; algorithms for parallel computing; evolutionary computations; genetic algorithms;

D O I：

10.7494/csci.2018.19.4.3006

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The selection of a proper set of views to materialize plays an important role in database performance. There are many methods of view selection that use different techniques and frameworks to select an efficient set of views for materialization. In this paper, we present a new efficient scalable method for view selection under the given storage constraints using a tree mining approach and evolutionary optimization. The tree mining algorithm is designed to determine the exact frequency of (sub)queries in the historical SQL dataset. The Query Cost model achieves the objective of maximizing the performance benefits from the final view set that is derived from the frequent view set given by the tree mining algorithm. The performance benefit of a query is defined as a function of query frequency, query creation cost, and query maintenance cost. The experimental results show that the proposed method is successful in recommending a solution that is fairly close to an optimal solution.

引用

页码：431 / 455

页数：25

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