Continuous monitoring of top-k dominating queries over uncertain data streams

Abstract

In many scenarios, e.g., environmental monitoring using multiple sensors, the uncertain data objects arrive continuously (online) and need to be processed in a streaming manner. We first formally define the problem of continuous probabilistic top-k dominating (PTOPK) query processing over uncertain data streams based on a count-based sliding window model. Based on the observation that PTOPK does not change dramatically in consequent sliding window and most uncertain data objects not in PTOPK cannot be inserted in PTOPK in a certain period of time, an efficient postponed examination algorithm (PEA) is proposed. With PEA, the scores calculation for some uncertain data objects not in PTOPK can be postponed and the computation cost can be saved. Extensive experiments have been conducted to demonstrate the efficiency of our approaches.