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- W2912042114 abstract "Map Reduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For large-scale data search problem in the distributed computing environment based on Map Reduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for large-scale data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the Map Reduce. And we extended HDFS design in Map Reduce architecture, which make it to achieve effective integration with Map Reduce. Then the algorithm can make the best of the scalability, high parallelism of Map Reduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining large-scale data." @default.
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- W2912042114 date "2012-04-06" @default.
- W2912042114 modified "2023-09-23" @default.
- W2912042114 title "The Algorithm of Ant Colony Parallel Search for Large-Scale Data Based on MapReduce" @default.
- W2912042114 doi "https://doi.org/10.1109/iceice.2012.809" @default.
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