In this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor trajectory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC’s IndoorGML standard. We drive the discussion on modeling issues that have been overlooked so far and illustrate them with a real-world case study concerning the Louvre Museum, in an effort to provide a pragmatic view of what the proposed model represents and how. We also present experimental results based on Louvre’s visiting data showcasing how state-of-the-art mining algorithms can be applied on trajectory data represented according to the proposed model, and outline their advantages and limitations. Finally, we provide a formal outline of a new sequential pattern mining algorithm and how it can be used for extracting interesting trajectory patterns.

在本文中，我们提出了一个新的轨迹概念模型，它考虑了语义和室内空间信息，并支持上下文感知移动数据挖掘和统计分析方法的设计和实施。受令人信服的博物馆案例研究以及我们认为缺乏室内轨迹研究的启发，我们将最先进的语义户外轨迹模型的各个方面与室内空间的语义化层次符号表示相结合，遵守OGC的IndoorGML标准。我们推动了对迄今为止被忽视的建模问题的讨论，并通过一个关于卢浮宫博物馆的真实案例研究来说明它们，以提供一个关于所提议模型代表什么以及如何代表的实用观点。我们还展示了基于卢浮宫访问数据的实验结果，展示了如何将最先进的挖掘算法应用于根据所提出的模型表示的轨迹数据，并概述了它们的优点和局限性。最后，我们提供了一个新的顺序模式挖掘算法的正式大纲，以及它如何用于提取有趣的轨迹模式。