Parallel frequent pattern growth algorithm optimization in cloud manufacturing environment
Dai Qing-hao · Computer Integrated Manufacturing Systems · 2012
Aiming at the massive data mining task in cloud manufacturing environment,the realization of existing parallel frequent pattern growth algorithm and its disadvantages were analyzed.By using key value store system,its counting and grouping parts were optimized.Based on simple,auto-increment and orderly manner of key value store system,the information of counting and grouping was stored on key value database.Through reducing the read-write of Distributed File System(DFS) and parallel executing the process of counting and grouping,the network and memory cost of storage node was decreased by optimization algorithm.On real datasets,the performance and file system I/O cost of algorithms before and after optimization were compared by experiments.