Non-index Based Skyline Analysis on High Dimensional Data with Uncertain Dimensions

Abstract

The notion of skyline query is to find a set of objects that is not dominated by any other objects. Regrettably, existing works lack on how to conduct skyline queries on high dimensional uncertain data with objects represented as continuous ranges and exact values, which in this paper is referred to as uncertain dimensions. Hence, in this paper we define skyline queries over data with uncertain dimensions and propose an algorithm, SkyQUD, to efficiently answer skyline queries. The SkyQUD algorithm determines skyline objects through three methods that guaranteed the probability of each object being in the final skyline results: exact domination, range domination, and uncertain domination. The algorithm has been validated through extensive experiments employing real and synthetic datasets. Results exhibit our proposed algorithm is efficient and scalable in answering skyline query on high dimensional and large datasets with uncertain dimensions.