This paper explores the top-k dominating query process on multiple uncertain data streams and employs the parallel computation to facilitate the query process. The challenges include how to quickly update the result and reduce the computation cost for processing uncertainty. By referring the related existing papers for certain data, we provide an effective top-k dominating query process on uncertain data streams in terms of time and space and the provided approach can be parallelized easily. After discussing the properties of the proposed approach, we validate our methods through extensive simulated experiments. The experimental results indicate that our algorithms can avoid the unnecessary computation effectively and reduce lots of communication throughput between servers, thus achieving the objective of updating the results quickly.