{"title":"云制造环境下基于Mahout的并行频繁模式生长算法优化","authors":"Jie Wang, Yu Zeng","doi":"10.1109/ISCID.2014.258","DOIUrl":null,"url":null,"abstract":"In cloud manufacturing environment, many manufacturing enterprises will produce massive data of a variety of forms. We do research of optimization parallel frequent pattern mining algorithm based on Mahout in this paper. We first analyze the implement and defects of PFP-Growth in Mahout. Then we propose two optimization strategies. One is parallel sequence optimization, and another is optimization the storage of counting information. Datasets from real manufacturing and Webdocs show the effectiveness of the strategy in time and space of the optimization.","PeriodicalId":385391,"journal":{"name":"2014 Seventh International Symposium on Computational Intelligence and Design","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The Optimization of Parallel Frequent Pattern Growth Algorithm Based on Mahout in Cloud Manufacturing Environment\",\"authors\":\"Jie Wang, Yu Zeng\",\"doi\":\"10.1109/ISCID.2014.258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In cloud manufacturing environment, many manufacturing enterprises will produce massive data of a variety of forms. We do research of optimization parallel frequent pattern mining algorithm based on Mahout in this paper. We first analyze the implement and defects of PFP-Growth in Mahout. Then we propose two optimization strategies. One is parallel sequence optimization, and another is optimization the storage of counting information. Datasets from real manufacturing and Webdocs show the effectiveness of the strategy in time and space of the optimization.\",\"PeriodicalId\":385391,\"journal\":{\"name\":\"2014 Seventh International Symposium on Computational Intelligence and Design\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Seventh International Symposium on Computational Intelligence and Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCID.2014.258\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Seventh International Symposium on Computational Intelligence and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID.2014.258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Optimization of Parallel Frequent Pattern Growth Algorithm Based on Mahout in Cloud Manufacturing Environment
In cloud manufacturing environment, many manufacturing enterprises will produce massive data of a variety of forms. We do research of optimization parallel frequent pattern mining algorithm based on Mahout in this paper. We first analyze the implement and defects of PFP-Growth in Mahout. Then we propose two optimization strategies. One is parallel sequence optimization, and another is optimization the storage of counting information. Datasets from real manufacturing and Webdocs show the effectiveness of the strategy in time and space of the optimization.
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