Spatial Join on Positional Uncertain Data

This paper presents a probabilistic spatial join on positional uncertain data designed to be a) generalist; b) accurate and c) efficient. A proposed progressive Monte Carlo algorithm is used in the refinement step and the Chebyshev inequality is applied in the filtering one in order to provide efficiency, efficacy and generality. The experiments show that the current propose is Pareto efficient concerning these requirements, i.e., it is not outperformed by any competing method. Also, the solution’s parameters relating accuracy and efficiency may be adjusted to maximize the gain in one while relaxing the other according to user’s demand.