计算机集成制造系统 ›› 2017, Vol. 23 ›› Issue (第12): 2571-2582.DOI: 10.13196/j.cims.2017.12.002

• 产品创新开发技术 • 上一篇    下一篇

面向协同任务的资源服务关键特征序列识别方法

李海波1,2,雷秀洋1,2   

  1. 1.华侨大学计算机科学与技术学院
    2.厦门市企业互操作与商务智能工程技术研究中心
  • 出版日期:2017-12-31 发布日期:2017-12-31
  • 基金资助:
    国家自然科学基金资助项目(71571056);福建省高校青年自然基金重点资助项目(JZ160409);泉州市科技计划资助项目(2015Z125)。

Identifying key feature sequence of resource services for collaborative task

  • Online:2017-12-31 Published:2017-12-31
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.71571056),the Natural Foundation for Young Scholars in the Universities of Fujian Province,China(No.JZ160409),and the Quanzhou Science and Technology Plan,China(No.2015Z125).

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摘要： 为挖掘频繁的资源服务序列,进而提高协同任务的资源选取效率,提出基于资源服务特征以及特征间影响关系的方法。针对资源服务序列,为求解资源服务间的影响度,提出一种关键特征序列识别方法。首先,采用PrefixSpan算法分析并获得资源服务序列模式;然后,针对资源服务序列提出基于时间序列的影响度计算方法,并从中识别出关键特征序列,进而计算出资源服务序列的综合影响度;最后,通过仿真实验分析验证了所提方法的可行性。

关键词: 资源服务序列, 关键特征序列, 影响度, 协同任务

Abstract: To mining the frequent Resource Service Sequence (RSS) and improve the resource selection efficiency,the method based on RSS and the influence between resource services was proposed.For RSS,an approach to identify key feature sequence was proposed to resolve the influence degree between resource services.By using PrefixSpan algorithm,the patterns of RSSs was analyzed and resolved.Based on time-series of executing business activities,the key feature sequences were identified for RSS,and the synthetical degree of influence was resolved.The feasibility of proposed approach was tested with simulation experiments.

Key words: resource service sequence, key feature sequence, influence degree, collaborative task

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