Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)

Gosain, Anjana; Sachdeva, Kavita

doi:10.4018/978-1-7998-2975-1.ch005

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…The current best positions are located, as well as kept up to date, and the information is expressed in condition. (5),…”

Section: Initialization Of Datamentioning

confidence: 99%

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

Vullam,

Murali Krishna,

Davuluri

et al. 2023

IJRITCC

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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.

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“…The current best positions are located, as well as kept up to date, and the information is expressed in condition. (5),…”

Section: Initialization Of Datamentioning

confidence: 99%

“…Memory space is the most significant challenge presented by the MV process due to the fact that it utilises a significant amount of space. The cost of maintaining MVs is another challenge [4,5]. It is essential to make an appropriate choice of views in order to achieve a faster response to the queries.…”

Section: Introductionmentioning

confidence: 99%

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

Vullam,

Murali Krishna,

Davuluri

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…Azgomi & Sohrabi (2019) proved the coral reef optimization (CRO) efficiency in selecting materialized views in a data warehouse by increasing the coverage rate of queries compared to other methods. Work proposed by Gosain & Sachdeva (2020a) simulated a random-walk GWO algorithm in finding optimal views. They optimized the cost function within space constraints and proved the scalability and efficiency of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization for Prioritized Materialized View Selection

Madaan

Gosain

2022

International Journal of Information Retrieval Research

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Selecting appropriate views that provide faster query response time is a critical decision in data warehouse design. Top-level users expect quick results from a data warehouse for faster decision-making to gain a competitive edge in business. Prioritizing a view can distinguish views required to answer top-level users’ queries from regular users and provide a better selection chance. The prioritized materialized view selection (PMVS) problem addresses how to utilize the given space to materialize prioritized views more relevant to users. Particle swarm optimization algorithm has been used to achieve minimized query processing costs. Evolutionary algorithms are widely known to solve complex optimization problems quickly by reaching a semi-optimal solution. This paper explores the performance of six evolutionary algorithms: particle swarm optimization, coral reef optimization, cuckoo search, ant colony optimization, grey wolf optimization, and artificial bee colony. The results of empirical and statistical analysis show that PSO, CRO, and GWO algorithms are best suited to solve PMVS.

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A Hybrid Metaheuristic Framework for Materialized View Selection in Data Warehouse Environments

Srinivasarao¹,

Satish²

2023

Int. J. Coop. Info. Syst.

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The huge response time taken by the analytical queries is one of the most challenging problems in a data warehouse. The main reason for this high response time is the enormous amount of data being queried and the complex nature of the queries. This problem can be addressed with Materialized View Selection (MVS), where the optimal views with low response time are selected. To this end, the possible views for the analytical queries are randomly defined in the search space. Since most of the existing solutions are formulated based on semi-optimal solutions, this work introduces a hybrid metaheuristic-based framework to address the issue reliably. The major gaps in the existing research works are the resource wastage issue and the space constraint issue. These gaps are aimed to be addressed by the proposed hybrid framework. The proposed work integrates a Genetic Algorithm (GA) with Aquila Optimizer (AO) to search for the most optimal views from the search space. The presented solution follows highly efficient exploratory and exploitation behaviors to optimize the considered problem. Based on an iterative procedure, the most optimal views are selected by the hybrid framework, and these views are evaluated for minimum cost and time. Finally, performance evaluations are carried out to signify the practicality of the proposed approach.

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Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)

Cited by 3 publications

References 43 publications

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

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

Evolutionary Optimization for Prioritized Materialized View Selection

A Hybrid Metaheuristic Framework for Materialized View Selection in Data Warehouse Environments

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