支持社会协同计算的跨组织工作流任务分派算法

发布时间：2019-02-14 23:49

【摘要】：针对社会协同计算带来的不可靠性和社会网络所固有的大规模性问题,提出了支持社会协同计算的跨组织工作流任务分派优化算法.首先,采用了基于工作流任务子网分层的优化模型,将复杂社会网络图进行有效地划分,从而简化了社会网络成员的协作关系评估问题;然后,根据划分后网络的拓扑特征,设计了一种基于工作流任务子网连接点的快速介数中心性计算方法,以高效地选取跨组织业务项目的领导者;最后,采用基于任务子网划分的最短路径近似算法,实现了快速查找跨组织业务过程的协作成员;并且,理论证明了支持社会协同计算的工作流分派算法的可行性.实验结果表明所提算法大幅降低了社会协同计算的复杂性,保证了较高的准确性,解决了工作流任务成员之间的关系评价和人工团队组合优化的时效性问题,为社会协同计算的任务分派提供了一种新的思路.
[Abstract]:Aiming at the unreliability of social cooperative computing and the inherent large-scale problem of social network, a cross-organizational workflow task assignment optimization algorithm is proposed to support social collaborative computing. Firstly, a hierarchical optimization model based on workflow task subnet is used to divide the complex social network graph effectively, which simplifies the evaluation of the cooperative relationship among the members of the social network. Then, according to the topological characteristics of the partitioned network, a fast meso-centric computing method based on workflow task subnet connection points is designed to select the leaders of cross-organizational business projects efficiently. Finally, the shortest path approximation algorithm based on task subnet partition is used to quickly find the cooperative members of cross-organizational business processes, and the feasibility of workflow assignment algorithm supporting social cooperative computing is proved theoretically. The experimental results show that the proposed algorithm greatly reduces the complexity of social collaborative computing, ensures higher accuracy, and solves the problem of the evaluation of the relationship between the task members of workflow and the time-efficiency of the optimization of the combination of artificial teams. It provides a new idea for the task assignment of social cooperative computing.
【作者单位】：南京航空航天大学计算机科学与技术学院;上海第二工业大学计算机与信息工程学院;
【基金】：国家自然科学基金项目(61672022,61272036) 安徽省高校自然科学基金重点项目(KJ2017A414)~~
【分类号】：TP301.6

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