  In many real-world applications such as business planning and sensor data
monitoring, one important, yet challenging, the task is to rank objects(e.g.,
products, documents, or spatial objects) based on their ranking scores and
efficiently return those objects with the highest scores. In practice, due to
the unreliability of data sources, many real-world objects often contain noises
and are thus imprecise and uncertain. In this paper, we study the problem of
probabilistic top-k dominating(PTD) query on such large-scale uncertain data in
a distributed environment, which retrieves k uncertain objects from distributed
uncertain databases(on multiple distributed servers), having the largest
ranking scores with high confidences. In order to efficiently tackle the
distributed PTD problem, we propose a MapReduce framework for processing
distributed PTD queries over distributed uncertain databases. In this MapReduce
framework, we design effective pruning strategies to filter out false alarms in
the distributed setting, propose cost-model-based index distribution mechanisms
over servers, and develop efficient distributed PTD query processing
algorithms. Extensive experiments have demonstrated the efficiency and
effectiveness of our proposed distributed PTD approach on both real and
synthetic data sets through various experimental settings.
