利用海量位置数据分析用户行为，挖掘用户的潜在价值越来越受到人们的关注。室外环境中已有较成熟的解决方案。针对室内空间中WiFi定位数据的精确度、鲁棒性不足等问题，对面向室内空间的语义轨迹提取方法进行了研究，能在减少错误、压缩原始位置数据的同时，增强轨迹的表达能力，使得更深入的室内时空数据挖掘成为可能。该文基于室内空间建模、数据清洗、事件提取和语义增强4个模块的框架提出了室内语义轨迹计算的方法，在真实数据集和模拟数据集上进行实验，结果表明：该方法能从存在误差和缺失的室内定位数据中，准确有效地挖掘和提取出含有语义信息的轨迹数据，为上层的应用分析所用。
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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