Cross-Organizational Workflow Task Allocation Algorithms for Socially Aware Collaborative Computing

Recently, human-interactions are substantial part of Web service-oriented collaborations and cross-organizational business processes. Social networks can help to process crowdsourced workflow tasks among humans in a more effective manner. However, it is challenging to identify a group of prosperous collaborative partners with a leader to work on joint cross-organizational workflow tasks in a prompt and efficient way, especially when the number of alternative candidates is large in collaborative networks. Therefore, in this paper, a new and efficient algorithm has been proposed to find an optimal group in social networks so as to process crowdsourced workflow tasks. Firstly, a set of new concepts has been defined to remodel the social graph; then, a sub-graph connector-based betweenness centrality algorithm has been enhanced to efficiently identify the leader who serves as the host manager of the joint workflow tasks; finally, an efficient algorithm is proposed to find the workflow task members associated with the selected leader by confining the searching space in the set of connector nodes. Theoretical analysis and extensive experiments are conducted for validation purpose; and the experimental results on real data show that our algorithms outperform several existing algorithms in terms of computation time in dealing with the increasing number of workflow task executing candidates.