A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments

作     者：Popuri Srinivasarao Aravapalli Rama Satish 

作者机构：School of Computer Science and EngineeringVIT-AP UniversityAmaravatiIndia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第11期

页      面：1527-1547页

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0802[工学-机械工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Materialization ensemble approach stochastic ranking optimization optimal view selection 

摘      要：Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt *** problem of materialized view(MV)selection relies on selecting the most optimal views that can respond to more queries *** work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from *** proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique(ECHT).The constraints such as self-adaptive penalty,epsilon(ε)-parameter and stochastic ranking(SR)are considered for constraint *** two constraints helped the proposed model select the finest views that minimize the objective ***,a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization(CHECO)is introduced to choose the optimal *** and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given *** combining these two algorithms,the proposed framework resulted in a highly optimized set of views with minimized *** cost functions are described to enable the algorithm to choose the finest solution from the problem ***,extensive evaluations are conducted to prove the performance of the proposed approach compared to existing *** proposed framework resulted in a view maintenance cost of 6,329,354,613,784,query processing cost of 3,522,857,483,566 and execution time of 226 s when analyzed using the TPC-H benchmark dataset.