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

Popuri Srinivasarao, Aravapalli Rama Satish*

School of Computer Science and Engineering, VIT-AP University, Amaravati, India

* Corresponding Author: Aravapalli Rama Satish. Email: email

Computer Systems Science and Engineering 2023, 47(2), 1527-1547. https://doi.org/10.32604/csse.2023.038951

Abstract

Responding to complex analytical queries in the data warehouse (DW) is one of the most challenging tasks that require prompt attention. The problem of materialized view (MV) selection relies on selecting the most optimal views that can respond to more queries simultaneously. This 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 DWs. The 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 handling. These two constraints helped the proposed model select the finest views that minimize the objective function. Further, a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization (CHECO) is introduced to choose the optimal MVs. Ebola and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given population. By combining these two algorithms, the proposed framework resulted in a highly optimized set of views with minimized costs. Several cost functions are described to enable the algorithm to choose the finest solution from the problem space. Finally, extensive evaluations are conducted to prove the performance of the proposed approach compared to existing algorithms. The 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.

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APA Style
Srinivasarao, P., Satish, A.R. (2023). A novel hybrid optimization algorithm for materialized view selection from data warehouse environments. Computer Systems Science and Engineering, 47(2), 1527-1547. https://doi.org/10.32604/csse.2023.038951
Vancouver Style
Srinivasarao P, Satish AR. A novel hybrid optimization algorithm for materialized view selection from data warehouse environments. Comput Syst Sci Eng. 2023;47(2):1527-1547 https://doi.org/10.32604/csse.2023.038951
IEEE Style
P. Srinivasarao and A.R. Satish, "A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments," Comput. Syst. Sci. Eng., vol. 47, no. 2, pp. 1527-1547. 2023. https://doi.org/10.32604/csse.2023.038951



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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