In many applications, including location-primarily based services, queries may not be particular. on this project, we study the problem of successfully computing variety aggregates in a multidimensional space when the question location is unsure. Specifically, for a query point Q whose place is uncertain and a set S of points in a multidimensional area, we need to calculate the aggregate (e.g., count, average and sum) over the subset S’ of S such that for each p ϵ S’, Q has at least possibility θ within the distance γ to p. We recommend novel, efficient strategies to solve the trouble following the filtering-and-verification paradigm. Specifically, two novel filtering strategies are proposed to effectively and correctly remove data factors from verification. Our comprehensive experiments based on each actual and synthetic data reveal the efficiency and scalability of our techniques.