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山东大学学报(工学版) ›› 2010, Vol. 40 ›› Issue (4): 153-158.

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Mintegration:一种针对大规模数据的并发数据集成方案

陈胜利1,李俊奎2,刘小东1   

  1. 1. 西安财经学院管理学院, 陕西 西安 710100; 2. 支付宝(中国)网络技术有限公司, 浙江 杭州 310000
  • 收稿日期:2009-08-27 出版日期:2010-08-16 发布日期:2009-08-27
  • 作者简介:陈胜利(1974-),男,湖北黄冈人,讲师,博士,主要研究方向为决策分析.E-mail:212101csl@163.com
  • 基金资助:

    陕西省自然科学基金资助项目(SJ08ZP14);陕西省教育厅自然基金资助项目(07JK244);陕西省自然科学基金(2009JM9008);陕西省教育厅科学研究计划资助项目(09JK437)

Mintegration: a solution to large volume concurrent data integration

CHEN Sheng-li 1, LI Jun-kui2, LIU Xiao-dong1   

  1. 1. School of Management, Xi′an University of Finance and Economics, Xi′an 710100, China;
    2. Alipay Network Technology Co. Ltd., Hangzhou 310000, China
  • Received:2009-08-27 Online:2010-08-16 Published:2009-08-27

摘要:

针对现有数据集成方案不能解决有限内存与大规模数据量之间的矛盾,提出一种针对大规模数据的并发数据集成方案,该方案并不一次性将数据读入内存,而是将整个任务分成若干子任务,各子任务并发执行,从而解决了有限内存与大规模数据量之间的矛盾。理论和实验结果表明,这种集成方案能够以可配置的方式进行大规模的数据集成。

关键词: 数据集成, 大规模数据, 任务分解

Abstract:

In the light of the conflict between limited memory and large-scale data, a solution to large volume concurrent data integration was proposed, which was not read the data into memory at one time, but divided the task into several sub-tasks according to the large volume data. The sub-tasks was complicated by the implementation so as to solve the limited memory and largescale conflict between the amount of data. The theoretical analysis and empirical experiments showed that resources could be used in the method of the system, and the solution could be efficiently applied in the large volume data integration process with a configurable mode.

Key words: data integration, large volume data, task decomposition

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