计算机工程 ›› 2018, Vol. 44 ›› Issue (5): 7-13.doi: 10.19678/j.issn.1000-3428.0046728

• 先进计算与数据处理 • 上一篇    下一篇

基于时空分析的突发事件检测方法

梁月仙 1,2,3,陈自岩 1,2,王洋 1,2,张跃 1,2,3,郭智 1,2   

  1. 1.中国科学院 空间信息处理与应用系统技术重点实验室,北京 100190; 2.中国科学院电子学研究所,北京 100190; 3.中国科学院大学,北京 100190
  • 收稿日期:2017-04-10 出版日期:2018-05-15 发布日期:2018-05-15
  • 作者简介:梁月仙(1991—),女,硕士,主研方向为文本数据挖掘;陈自岩、王洋,助理研究员;张跃,博士;郭智,研究员。
  • 基金项目:

    国家自然科学基金(41501485)。

Bursty Event Detection Method Based on Spatio-temporal Analysis

LIANG Yuexian 1,2,3,CHEN Ziyan 1,2,WANG Yang 1,2,ZHANG Yue 1,2,3,GUO Zhi 1,2   

  1. 1.Key Laboratory of Technology in Geo-spatial Information Processing and Application System,Chinese Academy of Sciences,Beijing 100190,China; 2.Institute of Electronics,Chinese Academy of Sciences,Beijing 100190,China; 3.University of Chinese Academy of Sciences,Beijing 100190,China
  • Received:2017-04-10 Online:2018-05-15 Published:2018-05-15

摘要:

现有突发事件检测方法多数未考虑事件的重要性,且以孤立的方式看待事件的突发时间域和空间域。为此,提出一种基于时空要素综合分析的突发事件检测方法。引入数据立方体结构存储事件词,通过基于语义相似性的实时事件聚类算法抽取出重要事件。根据TFIDF计算事件在时空维度上的出现权重,给出有限状态机-高斯分布模型识别时空突发事件。实验结果表明,该方法能够有效地识别出事件的突发时间段和突发区域,与现有突发事件检测方法相比,检测突发事件的准确率更高。

关键词: 突发事件, 时空分析, 事件抽取, 实时事件聚类, 数据立方体

Abstract:

The existing bursty event detection method does not consider the importance of the eveuts,and treats the bursty event time domain and spatial domain of the incident in an isolated manner,and proposes an incident detection method based on comprehensive analysis of spatio-temporal elements.The data cube structure is introduced to store event words,and important events are extracted by a real-time event clustering algorithm based on semantic similarity.TFIDF is used to calculate the occurrence weights of events in the space-time dimension,and the finite state machine-Gaussian distribution model is used to identify spatio-temporal events.Experimental results show that the method can effectively identify bursty time and bursty area of the event,compared with the existing emergency detection method,the accuracy of detecting eveuts is higher.

Key words: bursty event, spatio-temporal analysis, event extraction, real-time event clustering, data cube

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