This paper presents a method to perform a location recommendation based on multiple criteria allowing noised coordinates. More specifi cally, the skyline query is adapted to handle those noises by modeling the errors of georeferenced points with an appropriate probability distribution and modifying the traditional dominance criterion used by that technique. The method is applied to a scenario in which the coordinates are set by a geocoding process in a sample of schools in a specifi c Brazilian city. It enables one to choose the level of confi dence in which a point is removed from the skyline solution (the location recommendation).

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.