Materialized view selection using artificial bee colony optimization

被引：18
作者：
Arun B. [1 ]
Vijay Kumar T.V. [1 ]
机构：
[1] Jawaharlal Nehru University, School of Computer and Systems Sciences, New Delhi
来源：
| 2017年 / IGI Global卷 / 13期
关键词：
Artificial Bee Colony Optimization; Data Warehouse; Decision Making; Materialized View Selection; Swarm Intelligence;
D O I：
10.4018/IJIIT.2017010102
中图分类号：
学科分类号：
摘要：
Data warehouse is an essential component of almost every modern enterprise information system. It stores huge amount of subject-oriented, time-stamped, non-volatile and integrated data. It is highly required of the system to respond to complex online analytical queries posed against its data warehouse in seconds for efficient decision making. Optimization of online analytical query processing (OLAP) could substantially minimize delays in query response time. Materialized view is an efficient and effective OLAP query optimization technique to minimize query response time. Selecting a set of such appropriate views for materialization is referred to as view selection, which is a nontrivial task. In this regard, an Artificial Bee Colony (ABC) based view selection algorithm (ABCVSA), which has been adapted by incorporating N-point and GBFS based N-point random insertion operations, to select Top-K views from a multidimensional lattice is proposed. Experimental results show that ABCVSA performs better than the most fundamental view selection algorithm HRUA. Thus, the views selected using ABCVSA on materialization would reduce the query response time of OLAP queries and thereby aid analysts in arriving at strategic business decisions in an effective manner. Copyright © 2017, IGI Global.
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页码：26 / 49
页数：23
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