Research on Materialized View Selection Algorithm in Data Warehouse

2009 ◽  
Author(s):  
Zhou Lijuan ◽  
Ge Xuebin ◽  
Wang Linshuang ◽  
Shi Qian
Keyword(s):  
Data Warehouse ◽  
Selection Algorithm ◽  
View Selection ◽  
Materialized View ◽  
Materialized View Selection

Materialized View Selection in the Data Warehouse

2010 ◽  
Vol 29-32 ◽  
pp. 1133-1138 ◽  
Author(s):  
Li Juan Zhou ◽  
Hai Jun Geng ◽  
Ming Sheng Xu
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Materialized Views ◽  
Storage Space ◽  
View Selection ◽  
Materialized View ◽  
Query Response Time ◽  
Materialized View Selection ◽  
Optimal Efficiency ◽  
The Cost

A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing decision-support or OLAP queries. Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. The goal is to select an appropriate set of views that minimizes sum of the query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space, etc. In this article, we present an improved PGA algorithm to accomplish the view selection problem; the experiments show that our proposed algorithm shows it’s superior.

Materialized View Selection Using Set Based Particle Swarm Optimization

2018 ◽  
Vol 12 (3) ◽  
pp. 18-39 ◽  
Author(s):  
Amit Kumar ◽  
T.V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Data Warehouse ◽  
Response Times ◽  
Particle Swarm ◽  
Selection Algorithm ◽  
Swarm Optimization ◽  
Continuous Growth ◽  
View Selection ◽  
Materialized View Selection

A data warehouse is a central repository of historical data designed primarily to support analytical processing. These analytical queries are exploratory, long and complex in nature. Further, the rapid and continuous growth in the size of data warehouse increases the response times of such queries. Query response times need to be reduced in order to speedup decision making. This problem, being an NP-Complete problem, can be appropriately dealt with by using swarm intelligence techniques. One such technique, i.e. the set-based particle swarm optimization (SPSO), has been proposed to address this problem. Accordingly, a SPSO based view selection algorithm (SPSOVSA), which selects the Top-K views from a multidimensional lattice, is proposed. Experimental based comparison of SPSOVSA with the most fundamental view selection algorithm shows that SPSOVSA is able to select comparatively better quality Top-K views for materialization. The materialization of these selected views would improve the performance of analytical queries and lead to efficient decision making.

Materialized View Selection using Marriage in Honey Bees Optimization

2015 ◽  
Vol 5 (3) ◽  
pp. 1-25 ◽  
Author(s):  
Biri Arun ◽  
T.V. Vijay Kumar
Keyword(s):  
Data Warehouse ◽  
Honey Bees ◽  
Response Times ◽  
Large Data ◽  
Strategic Decision ◽  
Selection Algorithm ◽  
View Selection ◽  
Materialized View Selection ◽  
On Line ◽  
Selection Of

Data warehouse was designed to cater to the strategic decision making needs of an organization. Most queries posed on them are on-line analytical queries, which are complex and computation intensive in nature and have high query response times when processed against a large data warehouse. This time can be substantially reduced by materializing pre-computed summarized views and storing them in a data warehouse. All possible views cannot be materialized due to storage space constraints. Also, an optimal selection of subsets of views is shown to be an NP-Complete problem. This problem of view selection has been addressed in this paper by selecting a beneficial set of views, from amongst all possible views, using the swarm intelligence technique Marriage in Honey Bees Optimization (MBO). An MBO based view selection algorithm (MBOVSA), which aims to select views that incur the minimum total cost of evaluating all the views (TVEC), is proposed. In MBOVSA, the search has been intensified by incorporating the royal jelly feeding phase into MBO. MBOVSA, when compared with the most fundamental greedy based view selection algorithm HRUA, is able to select comparatively better quality views.

Research on Materialized Views under Disk-Space Constraint in Data Warehouse

2014 ◽  
Vol 989-994 ◽  
pp. 1955-1958
Author(s):  
Lei Ma
Keyword(s):  
Data Warehouse ◽  
Space Constraint ◽  
Maintenance Cost ◽  
Materialized Views ◽  
Ranking Algorithm ◽  
Disk Space ◽  
View Selection ◽  
Materialized View ◽  
Processing Cost ◽  
Materialized View Selection

Materialized view is an important topic in data warehouse research, and also affects the query efficiency and maintenance cost. The disk-space view-selection problem is to select a set of materialized views for the purpose of minimizing the total query processing cost and the total maintenance cost. In this paper we introduce evolutionary algorithm using stochastic ranking algorithm, which can enable materialized view selection under disk-space constraint. The algorithm improve the stochastic ranking algorithm, which can find a near-optimal feasible solution. This paper use the algorithm in Police data warehouse.

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