The range searching problem is fundamental in a wide spectrum of applications such as radio frequency identification (RFID) networks, location based services (LBS), and global position system (GPS). As the uncertainty is inherent in those applications, it is highly demanded to address the uncertainty in the range search since the traditional techniques cannot be applied due to the inherence difference between the uncertain data and traditional data. In the paper, we propose a novel indexing structure, named U -Quadtree, to organize the uncertain objects in a multi-dimensional space such that the range searching can be answered efficiently by applying filtering techniques. Particularly, based on some insights of the range search on uncertain data, we propose a cost model which carefully considers various factors that may impact the performance of the range searching. Then an effective and efficient index construction algorithm is proposed to build the optimal U -Quadtree regarding the cost model. Comprehensive experiments demonstrate that our technique outperforms the existing works for range searching on multi-dimensional uncertain objects.
Zhang et al. proposed a quadtree-based index called U-Quadtree REF for range searching on multi-dimensional uncertain data.
Effectively indexing the multi-dimensional uncertain objects for range searching
EDBT '12
Computer Science