A novel coral reefs optimization algorithm for materialized view selection in data warehouse environments

Azgomi, Hossein; Sohrabi, Mohammad Karim

doi:10.1007/s10489-019-01481-w

Cited by 24 publications

(17 citation statements)

References 57 publications

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“…Being naïve, CRO is being explored in various applications and proven to solve complex optimization problems and MVS (Azgomi & Sohrabi, 2019) successfully. Therefore, the authors chose to check its efficacy for the PMVS problem.…”

Section: Cro -Coral Reef Optimization Algorithmmentioning

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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Section: Cro -Coral Reef Optimization Algorithmmentioning

confidence: 99%

Evolutionary Optimization for Prioritized Materialized View Selection

Madaan

Gosain

2022

International Journal of Information Retrieval Research

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“…Almost all recent studies that applied meta-heuristic methods to solve the CFr benchmark problem are listed in Table S. 6. This table summarizes the proposed methods, the number of function evaluations, the average, and the best results obtained in each study.…”

Section: Cro Evaluation In Mathematical Test Functionsmentioning

confidence: 99%

“…Most of them have focused on CRO-SL as a co-evolutionary algorithm [9,23,31,43]. Some others have combined CRO with: game theory [18], statistical approaches [14], simulated annealing [48], and memetic strategy [15], while some are still using the original version of CRO [3,6,19].…”

Section: Introductionmentioning

confidence: 99%

A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation

Emami

Nazif

Mousavi

et al. 2021

Journal of Environmental Management

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The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs' upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models' performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.

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“…There are several research works dealing with the design and modeling of data warehouses classified according to the type of database used for storage of data warehouse: works dealing with relational modeling [5] [6] [7] and others based on multidimensional modeling [8] [9]. Indeed, the nature, variety and volume of data in data warehouses are constantly increasing on the one hand and the need for analysts and queriers to optimize response time, storage cost and update time is becoming more and more of a challenge for developers of business intelligence systems.…”

Section: Introductionmentioning

confidence: 99%

Materialized Views Selection Problem in Decision Supporting Systems: Issues and Challenges

Mohamed¹,

Amnai²

2022

JCC

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The data warehouse is the most widely used database structure in many decision support systems around the world. This is the reason why a lot of research has been conducted in the literature over the last two decades on their design, refreshment and optimization. The manipulation of hypercubes (cubes) of data is a frequently used operation in the design of multidimensional data warehouses, due to their better adaptation to OLAP (On-Line Analytical Processing). However, the updating of these hypercubes is a very complicated process due mainly to the mass and complexity of the data presented. The purpose of this paper is to present the state of the art of works based on multidimensional modeling using the hypercube as a unit of presentation of data stores. It starts with the base of this process which is the choice of the views (cubes) forming our data warehouse base. The objective of this work is to describe the state of the art of research works dealing with the selection of materialized views in decision support systems.

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A novel coral reefs optimization algorithm for materialized view selection in data warehouse environments

Cited by 24 publications

References 57 publications

Evolutionary Optimization for Prioritized Materialized View Selection

Evolutionary Optimization for Prioritized Materialized View Selection

A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation

Materialized Views Selection Problem in Decision Supporting Systems: Issues and Challenges

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