Probabilistic Reverse Top-k Queries

Abstract

Ranking-aware query is one of the most fundamental queries in the database management field. The ranking query that returns top-k elements with maximal ranking scores according to a ranking function has been widely studied for decades. Recently, some researchers also focus on finding all customers who treat the given query object one of their top-k favorite elements, namely reverse top-k query. In such applications, each customer is described as a vector. However, none of the existing work has considered the uncertain data case for reverse top-k query, which is our focus. In this paper, we propose two methods to handle probabilistic reverse top-k query, namely BLS and ALS. As a basic solution, BLS approach checks each pair of user and product to find the query result. While as an advanced solution, ALS approach uses two pruning rules and historical information to significantly improve the efficiency. Both detailed analysis and experiments upon real and synthetic data sets illustrate the efficiency of our proposed methods.