Constrained k-closest pairs query processing based on growing window in crime databases
Shaojie Qiao, Changjie Tang, Huidong Jin, Shucheng Dai, Xingshu Chen · 2008
Spatial analysis in crime databases has recently been an active research topic. To solve the problem of finding the closest pairs of objects within a given spatial region, as required in crime geo-data applications, this paper proposes an efficient constrained k-closest pairs query processing algorithm based on growing window. It expands the window gradually instead of searching the whole workspace for multiple types of spatial objects. It employs a density-based range estimation approach to calculate the square query range and an optimized R-tree to store the index entities. In addition, a distance threshold T for the closest pair of objects is introduced to prune tree nodes. Experiments evaluate the effect of three important factors, i.e., the portion of overlapping between the workspaces of two data sets, the value of k, and the size of buffer. The results show that the new algorithm outperforms the heap-based approach.