Materialized view selection (MVS) improves the query processing efficiency and performance for making decisions effectively in a data warehouse. This problem is NP-hard and constrained optimization problem which involves space and cost constraint. Various optimization algorithms have been proposed in literature for optimal selection of materialized views. Few works exist for handling the constraints in MVS. In this study, authors have proposed the Cuckoo Search Algorithm (CSA) for optimization and Stochastic Ranking (SR) for handling the constraints in solving the MVS problem. The motivation behind integrating CS with SR is that fewer parameters have to be fine tuned in CS algorithm than in genetic and Particle Swarm Optimization (PSO) algorithm and the ranking method of SR handles the constraints effectively. For proving the efficiency and performance of our proposed algorithm Stochastic Ranking based Cuckoo Search Algorithm for Materialized View Selection (SRCSAMVS), it has been compared with PSO, genetic algorithm and the constrained evolutionary optimization algorithm proposed by Yu et al. SRCSAMVS outperforms in terms of query processing cost and scalability of the problem.
Selection of materialized view plays an important part in structuring decisions effectively in datawarehouse. Materialized view selection (MVS) is recognized as NP-hard and optimization problem, involving disk space and cost constraints. Numerous algorithms exist in literature for selection of materialized views. In this study, authors have proposed stochastic ranking (SR) method, together with Backtracking Search Optimization Algorithm (BSA) for solving MVS problem. The faster exploration and exploitation capabilities of BSA and the ranking method of SR technique for handling constraints are the motivating factors for proposing these two together for MVS problem. Authors have compared results with the constrained evolutionary optimization algorithm proposed by Yu et al. (IEEE Trans Syst Man Cybernet Part C Appl Rev 33(4):458-467, 2003). The proposed method handles the constraints effectively, lessens the total processing cost of query and scales well with problem size.
Optimal selection of materialized views is crucial for enhancing the performance and efficiency of data warehouse to render decisions effectively. Numerous evolutionary optimization algorithms like particle swarm optimization (PSO), genetic algorithm (GA), bee colony optimization (BCO), backtracking search optimization algorithm (BSA), etc. have been used by researchers for the selection of views optimally. Various frameworks like multiple view processing plan (MVPP), lattice, and AND-OR view graphs have been used for representing the problem space of MVS problem. In this chapter, the authors have implemented random walk grey wolf optimizer (RWGWO) algorithm for materialized view selection (i.e., RWGWOMVS) on lattice framework to find an optimal set of views within the space constraint. RWGWOMVS gives superior results in terms of minimum total query processing cost when compared with GA, BSA, and PSO algorithm. The proposed method scales well on increasing the lattice dimensions and on increasing the number of queries triggered by users.
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