轨迹大数据：数据、应用与技术现状

许佳捷; 郑凯; 池明旻; 朱扬勇; 禹晓辉; 周晓方

doi:10.11959/j.issn.1000-436x.2015318

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轨迹大数据：数据、应用与技术现状

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- 轨迹大数据：数据、应用与技术现状
- Trajectory big data:data,applications and techniques
- 通信学报 2015年36卷第12期页码：97-105
- 作者机构：
  
  1. 苏州大学计算机科学与技术学院，江苏苏州 215006
  2. 江苏省软件新技术与产业化协同创新中心，江苏南京 211102
  3. 复旦大学计算机科学技术学院上海市数据科学重点实验室，上海 201203
  4. 山东大学计算机科学与技术学院，山东济南 250101
- 作者简介：
  
  [ "许佳捷（1983-），男，北京人，博士，苏州大学副教授、硕士生导师，主要研究方向为大数据管理、时空数据库、海量数据存储、分布式计算、工作流系统。" ]
  [ "郑凯（1983-），男，山东淄博人，博士，苏州大学特聘教授、博士生导师，主要研究方向为大数据管理、社交媒体数据分析、时空数据库、不确定数据库、内存数据库、数据挖掘等。" ]
  [ "池明旻（1977-），女，福建三明人，博士，复旦大学副教授、硕士生导师，主要研究方向为数据科学、大数据、机器学习。" ]
  [ "朱扬勇（1963-），男，浙江金华人，博士，复旦大学教授、博士生导师，主要研究方向为数据科学、大数据、数据挖掘。" ]
  [ "禹晓辉（1977-），男，山东德州人，博士，山东大学教授、博士生导师，主要研究方向为大数据管理与分析，包括分布式流数据管理、时空数据挖掘、社会媒体数据分析等。" ]
  [ "周晓方（1963-），男，江苏无锡人，博士，苏州大学特聘教授，主要研究方向为空间数据库、多媒体数据库、数据质量、高性能数据处理及网络信息系统，以及这些技术在生物信息学、地理信息系统、移动对象管理、水文信息系统、医疗卫生系统、Web查询及视频数据检索等方面的应用。" ]
- 基金信息：
  
  国家重点基础研究发展计划（“973”计划）基金资助项目(2015CB352500);国家自然科学基金资助项目(61232006);国家自然科学基金资助项目(61402312);国家自然科学基金资助项目(71331005);国家自然科学基金资助项目(61272092);山东省科技发展计划基金资助项目(2014GGE27178)
- DOI：10.11959/j.issn.1000-436x.2015318
  中图分类号： TP392
- 网络出版日期：2015-12，
  
  纸质出版日期：2015-12-25
- 稿件说明：
移动端阅览
许佳捷, 郑凯, 池明旻, 等. 轨迹大数据：数据、应用与技术现状[J]. 通信学报, 2015,36(12):97-105.

Jia-jie XU, Kai ZHENG, Ming-min CHI, et al. Trajectory big data:data,applications and techniques[J]. Journal on communications, 2015, 36(12): 97-105.
许佳捷, 郑凯, 池明旻, 等. 轨迹大数据：数据、应用与技术现状[J]. 通信学报, 2015,36(12):97-105. DOI： 10.11959/j.issn.1000-436x.2015318.

Jia-jie XU, Kai ZHENG, Ming-min CHI, et al. Trajectory big data:data,applications and techniques[J]. Journal on communications, 2015, 36(12): 97-105. DOI： 10.11959/j.issn.1000-436x.2015318.

摘要

移动互联技术的飞速发展催生了大量的移动对象轨迹数据。这些数据刻画了个体和群体的时空动态性，蕴含着人类、车辆、动物的行为信息，对交通导航、城市规划、车辆监控等应用具有重要的价值。为了实现有效的轨迹数据价值提取，近年来学术界和工业界针对轨迹管理问题开展了大量研究工作，包括轨迹数据预处理，以解决数据冗余高、精度差、不一致等问题；轨迹数据库技术，以支持有效的数据组织和高效的查询处理；轨迹数据仓库，支持大规模轨迹的统计、理解和分析；最后是知识提取，从数据中挖掘有价值的模式与规律。因此，综述轨迹大数据分析，从企业数据、企业应用、前沿技术这3个角度揭示该领域的现状。

Abstract

The fast development of mobile internet has given rise to an extremely large volume of moving objects trajectory data.These data not only reflect the spatio-temporal mobility of individuals and groups

but may also contain the behavior information of people

vehicles animals

and other objects of interest.They are invaluable for route planning

urban planning and vehicle monitoring

etc.

and tremendous efforts have been made to support effective trajectory data management

including trajectory data pre-processing

which handles issues such as high redundancy

low precision and inconsistency of sampling; trajectory database technologies

concerning the efficient and effective storage of trajectory data and query processing; trajectory data warehousing

which supports the analytics on large-scale trajectory data;knowledge discovery

by which useful patterns can be extracted from trajectory data.A survey of trajectory big data analytics from three different aspects:data

applications and techniques is provided.

关键词

Keywords

references

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