Multi-objective materialized view selection using MOGA

2020 ◽  
Vol 11 (S2) ◽  
pp. 220-231 ◽  
Author(s):  
Jay Prakash ◽  
T. V. Vijay Kumar
Keyword(s):  
View Selection ◽  
Multi Objective ◽  
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.

Handling Constraints Using Penalty Functions in Materialized View Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 1-17
Author(s):  
Anjana Gosain ◽  
Kavita Sachdeva
Keyword(s):  
Query Processing ◽  
Penalty Function ◽  
Penalty Functions ◽  
Maintenance Cost ◽  
Penalty Function Method ◽  
View Selection ◽  
Materialized View ◽  
Processing Cost ◽  
Adaptive Penalty ◽  
Materialized View Selection

Materialized view selection (MVS) plays a vital role for efficiently making decisions in a data warehouse. This problem is NP-hard and constrained optimization problem. The authors have handled both the space and maintenance cost constraint using penalty functions. Three penalty function methods i.e. static, dynamic and adaptive penalty functions have been used for handling constraints and Backtracking Search Optimization algorithm (BSA) has been used for optimizing the total query processing cost. Experiments were conducted comparing the static, dynamic and adaptive penalty functions on varying the space constraint. The adaptive penalty function method yields the best results in terms of minimum query processing cost and achieves the optimality, scalability and feasibility of the problem on varying the lattice dimensions and on increasing the number of user queries. The authors proposed work has been compared with other evolutionary algorithms i.e. PSO and genetic algorithm and yields better results in terms of minimum total query processing cost of the materialized views.

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