Uncertain data has many different styles, such as relational data, semi structured data, streaming data, and moving objects. According to scenarios and data characteristics, tens of data models have been developed, stemming from the core possible world model that contains a huge number of the possible world instances with the sum of probabilities equal to 1. However, the number of the possible world instances is far greater than the volume of the uncertain database, making it infeasible to combine medial results generated from all of possible world instances for the final query results. Thus, some heuristic techniques, such as ordering, pruning, must be used to reduce the computation cost for the high efficiency. Uncertain data are inherent in some important applications. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. Crowdsourcing has emerged as an effective way to perform tasks that are easy for humans but remain difficult for computers. This project tackles the problem of processing top- K queries over uncertain data with the help of crowdsourcing for quickly converging to the real ordering of relevant results.