Finding Potential Objects in Uncertain Dataset by Using Competition Power
Sheng-Fu Yang, Guanling Lee, Shou‐Chih Lo · 網際網路技術學刊 · 2019
In the past studies, it has been proven that skyline queries and dominating queries are very useful in applications such as multi-preference analysis and multi-criteria decision making. In real applications, such as environmental monitoring and market analysis, the data often have uncertain characteristics, and the uncertainty of the data mainly comes from the data randomness, the limitation of the measuring instrument or the delay of updating the data. Therefore, in this paper, by using the dominance concept, we propose an efficient method to help users screen out better data objects in multi-dimensional uncertain dataset. Furthermore, an appropriate probability model is also proposed to objectively calculate the scores of uncertain data. To show the benefit of the approach, a set of experiment is performed on both synthetic and real datasets. According to the experimental results on real dataset, the proposed method can find the potential data objects efficiently.