Multi‐objective materialized view selection using flamingo search optimization algorithm

Abstract

A materialized view selection in data warehouse management is important to speed up query processing. The data presented in data warehouses is generally stored as a set of materialized views. The major challenge is determining which views to materialize and satisfy the response time with reduced cost functions. This paper proposed an effective multi objective cost model based flamingo search algorithm for materialized view selection in data warehouse design. The multiple view processing plan structure of the data warehouse describes the search space of problem in order to select the optimal materialized views. The proposed model evaluates a multi-objective optimization problem based on the cost functions resulting from materialization. The multiple objective functions of the proposed model are maintenance costs, current query processing costs, response cost and previous query processing costs. This model selects the top-k views for materialization by satisfying the mentioned multi-objective functions. The experimental results are simulated using the TPC-H dataset. The efficacy of proposed model is measured by comparing the obtained results of the proposed model with various existing approaches.

Multi-Strategy Improved Flamingo Search Algorithm for Global Optimization

“…Abraham et al [14] proposed an energy-efficient cluster head selection for wireless sensor networks based on the Flamingo Search Algorithm to improve the energy efficiency of wireless sensor networks. Srinivasarao et al [15] proposed an effective Flamingo Search Algorithm based on a multi-objective cost model to optimize the efficiency of instantiation view selection in data warehouse management. However, the aforementioned studies focus on the application study of the Flamingo Search Algorithm, and not much research has been conducted on the optimization of the algorithm.…”