@Article{csse.2023.038951,
AUTHOR = {Popuri Srinivasarao, Aravapalli Rama Satish},
TITLE = {A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {47},
YEAR = {2023},
NUMBER = {2},
PAGES = {1527--1547},
URL = {http://www.techscience.com/csse/v47n2/53680},
ISSN = {},
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.},
DOI = {10.32604/csse.2023.038951}
}