0


wherenext:轨迹模式挖掘位置预测

WhereNext: a Location Predictor on Trajectory Pattern Mining
课程网址: http://videolectures.net/kdd09_trasarti_wnlptpm/  
主讲教师: Roberto Trasarti
开课单位: 国家研究委员会
开课时间: 2009-09-14
课程语种: 英语
中文简介:
移动设备和基于位置的服务的普及导致移动数据量不断增加。这种副作用为分析运动行为的创新方法提供了机会。 本文提出了下一步, 这是一种旨在以一定的精度预测运动物体下一个位置的方法。该预测使用以前提取的名为 "轨迹模式" 的运动模式, 它是移动对象行为的简洁表示, 将其作为具有典型行驶时间的经常访问的区域序列。 通过正式的训练和测试过程, 构建和评估了一个名为 t 型模式树的决策树。该树是从包含特定区域的轨迹模式中学习的, 它可以用作新轨迹的下一个位置的预测器, 以找到树中最佳的匹配路径。提出了三种不同的最佳匹配方法对新的运动物体进行分类, 并对其对预测质量的影响进行了广泛的研究。 使用轨迹模式作为预测规则具有以下含义: (一) 学习取决于特定区域中所有可用对象的移动, 而不是对象的各个历史记录;(ii) 预测树本质上包含从数据中产生的时空属性, 这使我们能够定义严格地依赖于此类运动的属性的匹配方法。 此外, 我们还提出了一组其他度量值, 这些措施先验地评估了一组轨迹模式的预测能力。这些措施是在现实生活中的案例研究中调整的。最后, 给出了一套详尽的实际数据集实验和结果。
课程简介: The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data. This side effect provides the opportunity for innovative methods that analyze the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three different best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.
关 键 词: 决策树; 轨迹模式挖掘; 预测
课程来源: 视频讲座网公开课
最后编审: 2020-05-21:王淑红(课程编辑志愿者)
阅读次数: 198