A Parallel Adaptive Partial Materialization Method of Data Cube Based on Genetic Algorithm
نویسندگان
چکیده
Due to the limitation of computing and storage resources, online analysis of massive data is usually time consuming. Data cube materialization is an effective way to improve the performance of online analysis. Considering the potential parallelism of genetic algorithm and its good global searching ability, a materialization method of data cube based on genetic algorithm is proposed. This method selects materialized views by combining the partial materialization strategy and MapReduce, while the materialized views can be adjusted adaptively according to the query log. Experimental results show that this method adapts to the big data computing environment and it can select reasonable materialized views to improve the query efficiency.
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تاریخ انتشار 2016