Balanced Dominating Top-k Queries over Uncertain Data

Abstract

Uncertainty of data is inherent in many important applications. Effectively extracting valuable information to enable better decisions is important but not a trivial task over uncertain data. We have witnessed a great deal of significant researches for this purpose, such as top-k queries, skyline queries and dominated top-k queries. As for uncertainty, the common challenge that those researches face is to answer the ranking methods in consideration of user's function score and probability. In this paper, we propose a novel ranking method to select reliable and worthy results. In our method the coordinated and balanced degree of score and probability is also an evaluation target. After constructing of balance degree, we design the balanced dominating top-k query semantic and effective algorithms to identify the top-k answers. Comprehensive experiments with both real and synthetic data sets demonstrate the effectiveness and efficiency of our proposed approach.