Continuously maintaining approximate quantile summaries over large uncertain datasets

Chunquan Liang, Yang Zhang, Yanming Nie, Shaojun Hu

Abstract: Highlights•We define quantile over uncertain datasets in terms of probabilistic cardinality. We develop a novel algorithm, namely uGK, to compute approximate quantile summaries over uncertain datasets incrementally. We theoretically analysis the complexity of the uGK algorithm, and experimentally verify the efficiency of our algorithm. Using only little space, our uGK algorithm obtains summaries that can support any quantile query within a given error.