Selection of materialized views using stochastic ranking based Backtracking Search Optimization Algorithm

Gosain, Anjana; Sachdeva, Kavita

doi:10.1007/s13198-019-00812-x

Cited by 8 publications

(5 citation statements)

References 32 publications

(55 reference statements)

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“…The number of moving and searching process of searching agent are counted Update the location of moving process of searching agent using equation (8) Update the location of searching process of the search agent using equation (…”

Section: Evaluate the Fitness Of Each Viewsmentioning

confidence: 99%

“…Anjana Gosain and Kavita Sachdeva, 8 introduced a backtracking search optimization scheme based on stochastic ranking approach for MV selection. Different handling constraints were utilized as parameters to attain the updated MV selection.…”

Section: Related Workmentioning

confidence: 99%

“…The other structure utilized by MVS is the multiple view processing plan (MVPP). 8 The outcome of all the relational operations is the view in the MVPP design like AND-OR DAG. Only the processing node in the MVPP represents each of the views.…”

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confidence: 99%

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Multi‐objective materialized view selection using flamingo search optimization algorithm

Srinivasarao

Satish

2022

Softw Pract Exp

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A materialized view selection in data warehouse management is important to speed up query processing. The data presented in data warehouses is generally stored as a set of materialized views. The major challenge is determining which views to materialize and satisfy the response time with reduced cost functions. This paper proposed an effective multi objective cost model based flamingo search algorithm for materialized view selection in data warehouse design. The multiple view processing plan structure of the data warehouse describes the search space of problem in order to select the optimal materialized views. The proposed model evaluates a multi-objective optimization problem based on the cost functions resulting from materialization. The multiple objective functions of the proposed model are maintenance costs, current query processing costs, response cost and previous query processing costs. This model selects the top-k views for materialization by satisfying the mentioned multi-objective functions. The experimental results are simulated using the TPC-H dataset. The efficacy of proposed model is measured by comparing the obtained results of the proposed model with various existing approaches.

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Section: Evaluate the Fitness Of Each Viewsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multi‐objective materialized view selection using flamingo search optimization algorithm

Srinivasarao

Satish

2022

Softw Pract Exp

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“…According to the recent study [86], the standard BSA was coupled with [87] proposed a new algorithm, in which BSA is blended with stochastic ranking (SR) to minimise the MVS problems. This newly introduced algorithm is called SRBSAMVS.…”

Section: Bsa-based Mppt Algorithmmentioning

confidence: 99%

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Hassan

Rashid

2020

Applied Mathematics and Computation

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Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems.This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are twofold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and meta-analysis guide for the scholars who are working on improving BSA.In artificial intelligence (AI), besides the existing traditional symbolic and statistical methods [1], computational intelligence (CI) has emerged as a recent research area [2]. According to [3], CI deals with collections of nature-inspired algorithms and approaches that in turn have been used to deal with complex practical problems that cannot be feasibly or effectively solved by traditional methods. CI comprises three subfields: fuzzy logic, evolutionary computation, and neural network.The concept of swarm intelligence (SI) is a part of evolutionary computation. It deals with artificial and natural systems that comprise several individuals and possess the ability of self-organisation and decentralised control.This concept, which was primarily initiated by Gerardo Beni and Jing Wang in 1989 in the context of cellular robotic systems, is used in AI [4]. Typically, the SI system comprises a collection of agents. The agents interact with their own environment or with each other. SI syst...

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“…In most cases, a DW is made up of various points of view and gives responses to questions. Through the use of view selection, the total amount of time spent responding to queries can be cut [11]. The most significant drawback of a data warehouse is the increased amount of time needed to process 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

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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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Selection of materialized views using stochastic ranking based Backtracking Search Optimization Algorithm

Cited by 8 publications

References 32 publications

Multi‐objective materialized view selection using flamingo search optimization algorithm

Multi‐objective materialized view selection using flamingo search optimization algorithm

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

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

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