Semantic trajectory extraction framework for indoor space
LUO Xinyuan1, CHEN Xin1, SHOU Lidan1,2, CHEN Ke1,3, WU Yanjing4
1. Department of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; 2. CAD & CG State Key Lab, Zhejiang University, Hangzhou 310027, China; 3. Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, Hangzhou 310027, China; 4. Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
Abstract：Using massive location data to analyze user behavior and tap the potential value of customers is getting much attention. Mature solutions have been studied for outdoor space, while the accuracy and robustness of WiFi positioning data are main challenges for indoor space. The method of extracting the semantic trajectory for indoor space is studied. It can reduce error, compress original position data and enhance the expression capability of trajectory for indoor spatio-temporal data mining. Based on four modules including indoor space modeling, data cleansing, event extraction and semantic enhancement, a framework for calculating indoor semantic trajectory is proposed. Experiments are conducted on real and simulated datasets, and the results show that this method can accurately and effectively extract trajectory data containing semantic information from indoor positioning data with incompleteness and errors, which can be used for high-level analytical applications.
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2019, Vol. 59 
