Imperfect top-k skyline query with confidence level

Abstract

Uncertain, imprecise and noisy data arise in a number of domains including sensor networks and data integration. Skyline analysis is a powerful tool in a wide spectrum of real applications involving multi-criteria optimal decision making. The skyline operator aims at returning the most interesting objects in a database. Previous researches showed that the skyline size over uncertain data is too large to be exploited. In this paper, we propose an advanced skyline analysis over uncertain databases where uncertainty is modelled by the evidence theory. We particularly tackle the following two important issues: (1) model the skyline query over an evidential database (2) rank the evidential skyline result and retrieve k skyline objects that are expected to have the highest score with considering the confidence level of the objects. We also study its impact on the top-k result. The efficiency and effectiveness of our proposal are verified by extensive experimental results.