Probabilistic Group Skyline Processing over Uncertain Data

With the advancement of technology, the volume of data can be huge, but the transportation process may introduce uncertainties due to possible data loss. Addressing such uncertain data has become a primary research trend. Group skyline (G-Skyline) query, a variant of the skyline query, is used in multi-criteria decision-making and environmental monitoring to identify group of members that cannot be dominated by any other groups. This paper considers group skyline query processing on the uncertain data. The uncertainty of data complicates the computations and manipulating the groups instead of the individual data points makes the computation even harder. The Top-m Probabilistic Point Combination algorithm (TmPPC) is proposed to reduce the space and time required for computing the G-Skyline that may need all group combinations. By preprocessing data with DSG and selecting the top m groups with the highest probability, the proposed algorithm resolves the issues and significantly reduces the computation time and the space requirement as the experimental results demonstrated.