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Jiawei Han – One of the best experts on this subject based on the ideXlab platform.

  • Trajectory outlier detection a partition and detect framework
    International Conference on Data Engineering, 2008
    Co-Authors: Jae-gil Lee, Jiawei Han
    Abstract:

    Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for Trajectory data. Even worse, an existing Trajectory outlier detection algorithm has limited capability to detect outlying sub- trajectories. In this paper, we propose a novel partition-and-detect framework for Trajectory outlier detection, which partitions a Trajectory into a set of line segments, and then, detects outlying line segments for Trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a Trajectory database. Based on this partition-and-detect framework, we develop a Trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level Trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real Trajectory data.

  • Trajectory Outlier Detection
    , 2008
    Co-Authors: Jae-gil Lee, Jiawei Han
    Abstract:

    Outlier detection hasbeenapopular datamining task. However, there isalackofserious study onoutlier detection forTrajectory data. Evenworse, anexisting Trajectory outlier detection algorithm haslimited capability todetect outlying sub- trajectories. Inthis paper, wepropose anovel partition-and-detect framework forTrajectory outlier detection, whichpartitions a Trajectory intoasetofline segments, andthen, detects outlying line segments forTrajectory outliers. Theprimary advantage of this framework istodetect outlying sub-trajectories fromatra- jectory database. Basedonthis partition-and-detect framework, wedevelop aTrajectory outlier detection algorithm TRAOD.Our algorithm consists oftwophases: partitioning anddetection. For thefirst phase, wepropose a two-level Trajectory partitioning strategy thatensures bothhighquality andhighefficiency. For thesecondphase, we present a hybrid ofthedistance-based anddensity-based approaches. Experimental results demonstrate thatTRAOD correctly detects outlying sub-trajectories fromreal Trajectory data.

Anchen Miao – One of the best experts on this subject based on the ideXlab platform.

  • Trajectory Outlier Detection on Trajectory Data Streams
    IEEE Access, 2020
    Co-Authors: Keyan Cao, Yefan Liu, Gongjie Meng, Haoli Liu, Anchen Miao
    Abstract:

    The detection of abnormal moving on Trajectory data streams is an important task in spatio-temporal data mining. An outlier Trajectory is a Trajectory grossly different from others, meaning there are few or even no trajectories following a similar route. In this paper, we propose a lightweight method to measure the outlier in Trajectory data streams. Furthermore, we propose a basic algorithm (Trajectory Outlier Detection on Trajectory data Streams-TODS), which can quickly determine the nature of the Trajectory. Finally, we propose an Approximate algorithm (ATODS) to reduce the detection cost. It is space approximate algorithm which can effectively reduce the amount of calculation. The cost of ATODS algorithm can satisfy the demand of Trajectory data streams. Our method are verified using both real data and synthetic data. The results show that they are able to reduce the running time without reducing the accuracy.

Heng Tao Shen – One of the best experts on this subject based on the ideXlab platform.

  • Personalized semantic Trajectory privacy preservation through Trajectory reconstruction
    World Wide Web, 2017
    Co-Authors: Yan Dai, Jie Shao, Chengbo Wei, Dongxiang Zhang, Heng Tao Shen
    Abstract:

    Trajectory data gathered by mobile positioning techniques and location-aware devices contain plenty of sensitive spatial-temporal and semantic information, and can support many applications through data analysing and mining. However, attribute-linkage and re-identification attacks on such data may cause privacy leakage, and lead to unexpected serious consequences. Existing privacy preserving techniques for Trajectory data often ignore the different privacy requirements of different moving objects or largely scarify the availability of Trajectory data. In view of these issues, we propose an effective personalized Trajectory privacy preserving method which can strike a good balance between user-defined privacy requirement and data availability in off-line Trajectory publishing scenario. The main idea is to firstly label semantic attributes of all sampling points on the Trajectory and build a corresponding taxonomy tree, next extract sensitive stop points, then for different types of sensitive stop points, adopt different strategies to select the appropriate points of user interests to replace while considering user speed and avoiding reverse mutation, and finally publish the reconstructed Trajectory. Besides, to make our method more realistic we further consider possible obstacles appeared in the user space environment. In the experiments, average identification possibility, Trajectory semantic consistency and Trajectory shape similarity are taken as evaluation criteria, and the performance of our method is comprehensively evaluated. The results show that our method can improve the user Trajectory availability as much as possible, while effectively achieving the different Trajectory privacy requirements.