A Novel Hybrid Optimization With Ensemble Constraint Handling Approach for the Optimal Materialized Views

Abstract

The datawarehouse is extremely challenging to work with, as doing so necessitates a significant investment of both time and space. As a result, it is essential to enable rapid data processing in order to cut down on the amount of time needed to respond to queries that are sent to the warehouse. To effectively solve this problem, one of the significant approaches that should be taken is to take the view of materialization. It is extremely unlikely that all of the views that can be derived from the data will ever be materialized. As a result, view subsets need to be selected intelligently in order to enable rapid data processing for queries coming from a variety of locations. The Materialized view selection problem is addressed by the model that has been proposed. The model is based on the ensemble constraint handling techniques (ECHT). In order to optimize the problem, we must take into account the constraints, which include the self-adaptive penalty, the Epsilon ()-parameter, and the stochastic ranking. For the purpose of making a quicker and more accurate selection of queries from the data warehouse, the proposed model includes the implementation of an innovative algorithm known as the constrained hybrid Ebola with COATI optimization (CHECO) algorithm. For the purpose of computing the best possible fitness, the goals of "processing cost of the query," "response cost," and "maintenance cost" are each defined. The top views are selected by the CHECO algorithm based on whether or not the defined fitness requirements are met. In the final step of the process, the proposed model is compared to the models already in use in order to validate the performance improvement in terms of a variety of performance metrics.