Abstract
Materialized view selection is a major challenge in data warehouse management, and prioritized cube selection is further approach to find an optimal set of prioritized cubes under resource constraints. In this paper, we introduce a hybrid approach combining particle swarm optimization (PSO) algorithm with tabu search (TS) to solve the prioritized cube selection problem. Our proposed hybrid algorithm deals with PSO’s premature convergence problem through integration of TS local neighbourhood search, and thus significantly improves the solution quality. We also present a neighbourhood reduction strategy based on cube information obtained during PSO search to intensify the search of TS for better solutions. Finally, we prove the effectiveness of our proposed hybrid algorithm for high-dimensional prioritized cube selection problem by comparing the results with PSO algorithm results.
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Gosain, A., Madaan, H. (2021). An Effective Hybrid Approach for Solving Prioritized Cube Selection Problem Using Particle Swarm Optimization and Tabu Search. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-6584-7_34
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DOI: https://doi.org/10.1007/978-981-15-6584-7_34
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