Multi-Objective Materialized View Selection Using Discrete Genetic & Particle Swarm Optimization
Keywords:
Data Warehouse, discrete genetic operator, particle swarm optimization, Materialized View SelectionAbstract
Using a data warehouse method, data from several heterogeneous and dispersed operating systems (OLTP) is retrieved, converted, and put into a centralized repository. It is primarily used to process queries and thoroughly analyze data that is important to decision-makers. Therefore, it is crucial to make this data available as soon as possible. Here we have the concept of the Materialize perspective. Data warehouses often store their given data as a collection of materialized perspectives. The most difficult part is deciding which views should be realized and quickly with less expensive functions. This paper presents, Multi-Objective Discrete Genetic Particle Swarm Optimization (DGPSO) based Materialized View Selection. Using DGPSO (discrete genetic operator based particle swarm optimization), the top-k views from a multidimensional lattice are chosen. Among the various objective functions in the proposed method, response costs, management costs, current query processing costs, and past query processing costs. The DGPSO-based mineralization view selection algorithm is able to choose views of higher quality for materialization, as demonstrated by a comparison between it and the basic view selection algorithm based on testing.
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