ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (9): 1889-1902.doi: 10.7544/issn1000-1239.2018.20180132

所属专题: 2018优青专题

• 综述 • 上一篇    下一篇

大规模时序图数据的查询处理与挖掘技术综述

王一舒1,袁野1,刘萌1,王国仁2   

  1. 1(东北大学计算机科学与工程学院 沈阳 110004); 2(北京理工大学计算机学院 北京 100081) (yishuwang@stumail.neu.edu.cn)
  • 出版日期: 2018-09-01
  • 基金资助: 
    国家自然科学基金优秀青年科学基金项目(61622202);国家自然科学基金项目(61732003,61572119);中央高校基本科研业务费专项资金(N150402005,N171607010) This work was supported by the National Natural Science Foundation of China for Excellent Young Scientists (61622202), the National Natural Science Foundation of China (61732003, 61572119), and the Fundamental Research Funds for the Central Universities (N150402005,N171607010).

Survey of Query Processing and Mining Techniques over Large Temporal Graph Database

Wang Yishu1,Yuan Ye1,Liu Meng1,Wang Guoren2   

  1. 1(School of Computer Science and Engineering, Northeastern University, Shenyang 110004); 2(School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081)
  • Online: 2018-09-01

摘要: 时序图作为一种带有时间维度的图结构,在图数据的查询处理与挖掘工作中扮演着越来越重要的角色.与传统的静态图不同,时序图的结构会随时间序列发生改变,即时序图的边由时间激活.而且由于时序图上每条边都有记录时间的标签,所以时序图包含的信息量相较于静态图也更为庞大,这使得现有的数据查询处理方法不能很好地应用于时序图中.因此如何解决时序图上的数据查询处理与挖掘问题得到研究者们的关注.对现有的时序图上的查询处理与挖掘方法进行了综述,详细介绍了时序图的应用背景和基本定义,梳理了现有的时序图模型,并从图查询处理方法、图挖掘方法和时序图管理系统3个方面对时序图上现有的工作进行了详细的介绍和分析.最后对时序图上可能的研究方向进行了展望,为相关研究提供参考.

关键词: 时序图, 大规模图数据, 图数据查询处理, 图数据挖掘, 图数据管理系统

Abstract: A temporal graph, as a graph structure with time dimension, plays a more and more important role in query processing and mining of graph data. Different with the traditional static graph, structure of the temporal graph changes with the time series, that is to say the edge of temporal graph is activated by time. And each edge of the temporal graph has the label of recording time, which makes the temporal graph contain more information than the static graph, so the existing data query processing methods cannot be used in the temporal graph. Therefore how to solve the problem of query processing and mining on the temporal graph has attracted much attention of researchers. This paper summarizes the existing query processing and mining methods on temporal graphs. Firstly, this paper gives the application background and basic definition of temporal graph, and combs the existing three typical models which are used to model temporal graph in the existing works. Secondly, this paper introduces and analyzes the existing work on temporal graph from three aspects: graph query processing method, graph mining method and temporal graph management system. Finally, the possible research directions on temporal graph are prospected to provide reference for related research.

Key words: temporal graph, large-scale graph data, graph data query processing, graph data mining, graph data management system

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