Influence Computation for Indoor Spatial Objects

Yue Li, Guojie Ma, Shiyu Yang, Liping Wang, Jiujing Zhang

Abstract: Studies have shown that people spend more than 85% of their time in indoor spaces. Providing varies location-based services (LBS) for indoor space is of great demand and has drew attentions from both industry and academic. The influence computation for spatial objects is one of the important applications in LBS and has broad applications in indoor facility location and indoor marketing. The influence query has been studied extensively in outdoor spaces. However, due to the fundamental difference between indoor and outdoor space, the outdoor techniques can not be applied for indoor space. In this paper, we propose the first indoor influence computation algorithm IRV to efficiently process indoor influence query. The proposed algorithm is based on the state-of-art indoor index structure VIP-Tree and several pruning rules are also presented to reduce the computation cost. The experiment results on both real and synthetic data sets show our proposed method outperforms the baseline algorithm.