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An efficient algorithm for crowdsourcing workflow tasks to social networks

Crowdsourcing human tasks to social network helps organizations to mitigate the shortage of skill or expertise in workflow applications. In this paper, a novel crowdsourcing algorithm is proposed for efficient identifying human group in social networks, which addresses the challenge of querying suitable collaborative experts for cross organizational business processes in a prompt and efficient way. The proposed algorithm consists of two querying methods: One is efficiently identifying leader candidates in social network while the other focuses on optimized for interaction costs and labour costs of the expert team. The first method use betweenness centrality based on subgraph connectors that effectively reduce computational cost for ranking the leader candidates. On the other hand, the second method can identify collaborative partners by combining Skyline and precomputed interaction distance from reference points in social graphs. Theoretical analysis and extensive experiments on real and synthetically generated dataset demonstrate the effectiveness and scalability of the proposed methods.