Efficient Skyline Computation over Incomplete and Uncertain Data for Decision Making Systems
Sayda Elmi, Kian‐Lee Tan · 2020
Quality of service (QoS) has been considered as a significant criterion for selecting among functionally similar application software (AS). Choosing an AS hinges not only on price and functionality, but also on user preferences as well. The skyline queries have attracted tremendous amount of attention as they are a popular example of preference queries and they can retrieve the most interesting objects from a dataset. However, existent approaches are not sufficient where the delivered QoS attributes are inherently uncertain and incomplete. In this paper, we tackle the problem of the efficient skyline computing on uncertain and incomplete QoS. We represent each QoS attribute of an AS using an evidence distribution. We then develop appropriate algorithms to efficiently compute the skyline of an AS set. Finally, we present our experimental results that show the efficiency of the proposed algorithms.