An indexed non-probability skyline query processing framework for uncertain data

Abstract

Today, we are in the era of making multi-criteria decisions based on analysis of available data collected from autonomous databases that usually contain uncertain data. Skyline query technique returns a set of interesting objects (skylines) to the user by eliminating objects that are dominated by other objects within the database. Obviously, without doubt streamlining the process of processing skylines in providing answers to user-specified queries is inevitable. In this paper, we proposed SQUiD framework that combines an index-based technique with a prediction method to reduce the computational time for computing skylines over uncertain high-dimensional data. Through experimentations, results obtained clearly demonstrate the superiority of SQUiD framework over SkyQUD framework and BBIS algorithm.