Using quantum evolutionary based algorithm to solve materialized view selection problem
作者： Raouf Mayata , Abdelmadjid Boukra
关键词: Selection (genetic algorithm) 、 Volume (computing) 、 Artificial neural network 、 Structure (mathematical logic) 、 Materialized view 、 Space (commercial competition) 、 Constraint (information theory) 、 Algorithm 、 Data warehouse 、 Computer science
摘要: A Data warehouse is a structure that stores big amount of data. This data exploited in the best possible ways order to improve efficiency decision-making. The huge volume makes answering queries complex and time-consuming. Therefore, materialized views are used reduce query processing time. Since materializing all not possible, due space maintenance constraints, view selection became one crucial decisions designing for optimal efficiency. In this paper, authors propose Quantum Evolutionary based algorithm named QEAM solve (MVS) problem with storage constraint. experimental results show proposed compared well-known algorithms MVS
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