# Computing Influence of a Product through Uncertain Reverse Skyline

## TL;DR

This paper introduces uncertain reverse skyline queries to measure product influence in uncertain data, providing efficient algorithms and parallel processing methods that outperform existing approaches.

## Contribution

It proposes a novel uncertain reverse skyline query type and develops efficient pruning, indexing, and parallel algorithms for influence measurement in uncertain data environments.

## Key findings

- The proposed methods significantly outperform baseline approaches.
- Efficient pruning and indexing techniques improve query processing.
- Parallel algorithms enhance scalability and performance.

## Abstract

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.