In existing studies on decomposition-based complex task crowdsourcing e-markets, a complex task is first decomposed into a flow of simple subtasks and then the decomposed subtasks are allocated independently to different individual workers. However, such retail-style independent allocation of decomposed subtasks costs much time and the intermediate results of subtasks cannot be utilized by each other; moreover, the independent allocation does not consider the cooperation among assigned workers and the time-dependency relations among subtasks. To solve such a problem, this paper presents a novel batch allocation approach for decomposition-based complex task crowdsourcing in social networks, in which the similar subtasks of complex tasks are integrated into a batch that will be allocated to the same workers. In the presented approach, it is preferable that a batch of subtasks will be allocated to the workers within the same group or the workers with closer relations in a social network; moreover, the allocation will consider the time constraints of subtasks so that the deadlines of the whole complex tasks can be satisfied. This batch allocation optimization problem is proved to be NP-hard. Then, two types of heuristic approaches are designed: the lateral approach that does not consider the subordination relationship between subtasks and complex tasks and the longitudinal approach that considers such relationships. The experiments on real-world crowdsourcing datasets show that the two presented heuristic approaches outperform traditional retail-style allocation approach in terms of total payment by requesters, average income of assigned workers, cooperation efficiency of assigned workers, and task allocation time.

在现有的基于分解的复杂任务众包电子市场研究中，复杂任务首先分解为简单子任务流，然后将分解后的子任务独立分配给不同的个体工人。但是，这种零售方式对分解后的子任务的独立分配会花费大量时间，并且子任务的中间结果无法相互利用。此外，独立分配不考虑分配的工人之间的合作以及子任务之间的时间依赖关系。为了解决这个问题，本文提出了一种新的批处理方法，用于社交网络中基于分解的复杂任务众包，其中将复杂任务的相似子任务集成到一个批次中，该批次将分配给同一工人。在提出的方法中，最好将一批子任务分配给同一组中的工人或社交网络中关系密切的工人。此外，分配将考虑子任务的时间限制，以便可以满足整个复杂任务的期限。该批分配优化问题被证明是NP难的。然后，设计了两种类型的启发式方法：不考虑子任务和复杂任务之间从属关系的横向方法和考虑这种关系的纵向方法。在现实世界中的众包数据集上进行的实验表明，从请求者的总付款，分配的工人的平均收入，