  Understanding the influence of a product is crucially important for making
informed business decisions. This paper introduces a new type of skyline
queries, called uncertain reverse skyline, for measuring the influence of a
probabilistic product in uncertain data settings. More specifically, given a
dataset of probabilistic products P and a set of customers C, an uncertain
reverse skyline of a probabilistic product q retrieves all customers c in C
which include q as one of their preferred products. We present efficient
pruning ideas and techniques for processing the uncertain reverse skyline query
of a probabilistic product using R-Tree data index. We also present an
efficient parallel approach to compute the uncertain reverse skyline and
influence score of a probabilistic product. Our approach significantly
outperforms the baseline approach derived from the existing literature. The
efficiency of our approach is demonstrated by conducting extensive experiments
with both real and synthetic datasets.
