Probabilistic Reverse Nearest Neighbors on Uncertain Data Streams

Abstract

hi these days, many applications of IoT, Big Data, and Cloud computing have been proposed and discussed. One of the common important issues in these applications is data processing. The data to be considered now have the properties of velocity, veracity, and volume. Uncertain data streams simultaneously have these properties. Reverse nearest neighbors (RNN) query finds the objects that have the query as the nearest object and plays an important in many applications. We thus study probabilistic RNN (PRNN) query over the uncertain data streams in this paper. Due to the uncertainty. the computation overhead for updates gets much higher and how to reduce the computation cost becomes challenging. We provide efficient PRNN query processes over uncertain data streams in terms of time. Our methods can effectively prune the irrelevant data objects and keep only the possible ones for further updates. thus saving time. The proposed methods are also validated through extensive simulation experiments.