Batch allocation for decomposition-based complex task crowdsourcing e-markets in social networks

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.