Materialized View Selection Using Bumble Bee Mating Optimization

Arun, Biri; Kumar, T. V. Vijay

doi:10.4018/ijdsst.2017070101

Cited by 24 publications

(10 citation statements)

References 42 publications

(11 reference statements)

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“…Thus, it can be reasonably inferred that QIEVSA is able to select reasonably good quality views, for higher dimensional data sets, that are capable of reducing the response time of analytical queries, which thereby would lead to efficient decision making. As future work, QIEVSA would be compared to existing swarm-based view selection algorithms [99][100][101][102][103][104][105][106][107].…”

Section: Discussionmentioning

confidence: 99%

A novel quantum-inspired evolutionary view selection algorithm

Kumar¹,

Kumar

2018

Sādhanā

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A data warehouse (DW) is designed primarily to meet the informational needs of an organization's decision support system. Most queries posed on such systems are analytical in nature. These queries are long and complex, and are posed in an exploratory and ad-hoc manner. The response time of these queries is high when processed directly against a continuously growing DW. In order to reduce this time, materialized views are used as an alternative. It is infeasible to materialize all views due to storage space constraints. Further, optimal view selection is an NP-Complete problem. Alternately, a subset of views, from amongst all possible views, needs to be selected that improves the response time for analytical queries. In this paper, a quantum-inspired evolutionary view selection algorithm (QIEVSA) that selects Top-K views from a multidimensional lattice has been proposed. Experimental comparison of QIEVSA with other evolutionary view selection algorithms shows that QIEVSA is able to select Top-K views that are comparatively better in reducing the response times for analytical queries. This in turn aids in efficient decision making.

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Section: Discussionmentioning

confidence: 99%

A novel quantum-inspired evolutionary view selection algorithm

Kumar¹,

Kumar

2018

Sādhanā

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“…Another research is addressed in Arun et al 22 to select the perfect sets of materialized views using the Bumble bee mating optimization technique. In this, the developed scheme chooses top‐k views to diminish the response time of query answering.…”

Section: Related Workmentioning

confidence: 99%

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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“…(Mami et al, 2012) presented a survey of various view selection algorithms and also illustrated a classification of these algorithms. View selection problem in the context of the data warehouse has been solved using metaheuristic algorithms (Arun & Vijay Kumar, 2015a, 2015b, 2017a, 2017bVijay Kumar & Arun, 2016, 2017, Vijay Kumar & Kumar, 2014, 2015Kumar & Vijay Kumar, 2018). The same problem was formulated as a bi-objective view selection problem and solved using multi-objective evolutionary algorithms VEGA (Prakash & Vijay Kumar, 2019a), MOGA (Prakash & Vijay Kumar, 2020a), SPEA-2 (Prakash & Vijay Kumar, 2019b) and NSGA-II (Prakash & Vijay Kumar, 2020b).…”

Section: View Materializationmentioning

confidence: 99%

Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary Algorithm

Kumar

2022

Journal of Information Technology Research

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Big data view materialization enhances the performance of Big data queries. This is a complex problem due to large volume, heterogeneity, high rate of data generation, low integrity and low value of Big data. Big data view materialization is a bi-objective optimization problem with the objectives - minimization of query evaluation time for a set of workload queries over a window of time and minimization of update processing cost of the views. Structure of Big data views can be represented as directed graph, which can be used to identify the candidate Big data views for a given set of queries. Evolutionary algorithms can be used to solve the problem of Big data view materialization. This paper presents an algorithm based on Strength Pareto Evolutionary Algorithm (SPEA-2) to generate a set of optimal solutions to the bi-objective Big data view selection problem.

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Materialized View Selection Using Bumble Bee Mating Optimization

Cited by 24 publications

References 42 publications

A novel quantum-inspired evolutionary view selection algorithm

A novel quantum-inspired evolutionary view selection algorithm

Multi‐objective materialized view selection using flamingo search optimization algorithm

Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary Algorithm

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