Theoretical frameworks for improving the computation of skylines over uncertain data

Abstract

Skyline query is preeminent for realizing solutions that support multi-criteria deci-
sion-making, particularly for modern applications that often capture or generate a large
volume of high-dimensional databases. The essence of the skyline query is to return a
set of objects that are not dominated by any other objects. In this article, we proposed
frameworks namely; Skyline Query on Uncertain Database, Constrained Skyline Query
on Uncertain Database, and Abating the computation of constrained Skyline Query on
Uncertain Database that deal with the computational problem associated with processing
constrained skyline query over uncertain data with high dimensionality. Although solu-
tions for processing such queries over uncertain data are available, they are inefficient as
they do not utilize the result of earlier computations. Since multiple constrained skyline
queries might span over the same part of the database, this research work attempt to
avoid unnecessary skyline computations of these queries by identifying the conditions
specified in these queries.