An Efficient Optimization Approach for Top-k Queries on Uncertain Data

Abstract

Uncertain data is inherent in various important applications and Top-[Formula: see text] query on uncertain data is an important query type for many applications. To tackle the performance issue of evaluating Top-[Formula: see text] query on uncertain data, an efficient optimization approach was proposed in this paper. This method can anticipate the tuples most likely to become Top-[Formula: see text] result based on dominant relationship analysis, greatly reducing the amount of data in query processing. When the database is updated, this method could determine whether the change affects the current query result, and help us to avoid unnecessary re-query. The experimental results prove the feasibility and effectiveness of this method.