III: Small: Semantic Trajectory Mining with Contexts

传感和定位技术的快速发展为我们提供了越来越多的从人类运动、动物痕迹和交通中收集的轨迹数据。了解这种大规模的轨迹数据及其周围环境（例如，位置信息、当地事件、天气和环境）可以使许多重要的应用受益。例如，对人类轨迹的语义理解可以帮助分析一个人的兴趣、社会经济地位和健康状况；挖掘交通模式W.R.T.当地事件和天气条件可以导致更有弹性的交通系统；研究动物的运动如何对环境变化做出反应可以促进我们对生态系统的理解。本项目研究数据挖掘算法，并为丰富时空背景下的语义轨迹挖掘提供解决方案。通过PI的跨学科合作，研究结果将对社会科学、卫生、交通和生态学等其他学科产生更广泛的影响。该项目寻求轨迹数据挖掘的创新解决方案。现有的轨迹数据挖掘方法在很大程度上局限于对轨迹数据的研究，而没有考虑到丰富的时空上下文信息。这种限制通常会导致检测到琐碎甚至错误的模式。例如，如果考虑到大型足球比赛或极端天气等环境，异常情况（例如繁忙的交通或绕行轨迹）实际上可能是预期的。为了将上下文纳入轨迹挖掘，关键的挑战是在给定轨迹和上下文的稀疏和噪声观测的情况下，对上下文和轨迹之间的隐含和复杂相关性进行建模。为了正确、充分地利用丰富的语境对轨迹数据进行分析，本项目有两个研究目标：(1)基于语境的轨迹标注和(2)基于语境的轨迹挖掘。在第一个目标中，目标是将轨迹与相关的静态或动态环境联系起来。在第二个目标中，研究人员将重新审视轨迹挖掘算法，并创新三种重要的挖掘技术：递归模式挖掘、异常检测和轨迹聚类。将开发新的数据挖掘技术，以便在数据挖掘过程中合并带注释的上下文。该项目将推进轨迹数据挖掘的最新技术，并丰富一般数据挖掘原理。

Rapid advances of sensing and positioning technologies have provided us with an increasing amount of trajectory data collected from human movements, animal traces, and traffic. Understanding such large-scale trajectory data together with their surrounding contexts (e.g., location information, local events, weather and environment) could benefit a number of important applications. For example, a semantic understanding of human trajectories can help profiling a person's interest, socioeconomic status and health conditions; mining traffic patterns w.r.t. local events and weather conditions can lead to a more resilient transportation system; and studying how animal movements respond to environmental changes can advance our understanding of the ecological system. This project investigates data mining algorithms and provides solutions toward semantic trajectory mining with rich spatial-temporal contexts. The results will have broader impacts in other disciplines such as social science, health, transportation, and ecology through PI's interdisciplinary collaborations.This project seeks innovative solutions for trajectory data mining. State-of-the-art trajectory data mining methods have been largely limited to studying only trajectory data without considering the rich spatial-temporal contextual information. Such a limitation often results in detecting trivial or even erroneous patterns. For example, anomalies (e.g., heavy traffic or detouring trajectories) may actually be expected if considering the contexts such as big football games or extreme weather. To incorporate contexts in trajectory mining, the key challenge is to model the implicit and complications correlations between contexts and trajectories given the sparse and noisy observations on both trajectories and contexts. To correctly and fully utilize the rich contexts in analyzing trajectory data, this project has two research aims: (1) trajectory annotation using contexts and (2) trajectory mining with contexts. In the first aim, the objective is to associate trajectories with relevant static or dynamic contexts. In the second aim, the researchers will re-visit trajectory mining algorithms and innovate three important mining techniques: recurrence pattern mining, anomaly detection, and trajectory clustering. New data mining techniques will be developed to incorporate the annotated contexts in the data mining process. The project will advance the state of the art in trajectory data mining and enrich general data mining principles.