摘要
流量可视化对于网络研究和监管人员侦测网络异常,了解网络中流量的特征和趋势等有着重要的意义。现有的流量可视化技术不能满足日益广泛的流量可视化需求。针对流量数据的时间、维度、结构等特征,分别提出了螺旋可视化技术、维度分类方法和空间填充技术来满足不同特征下的可视化需求。通过对4over6项目中流量数据可视化,并与目前使用的技术进行对比分析,得知时间特征中螺旋线技术较直线映射技术更易发现数据周期特性;维度特征中,维度分类较之于平行坐标技术,更能适应特殊维度要求;结构特征中,空间填充技术较之于仅呈现结构特性的结点链路技术,能同时实现结点信息的呈现。
Flow visualization is of great help for network researchers and supervisors to detect network anomalies, understand the characteristics of network traffic and find time trend, etc. Existing flow visualization technologies have failed to fully meet the growing needs of traffic visualization. Aiming at time, dimension and structure charac- teristics of flow, this paper adopts spiral visualization technology, dimension classification method and space filling technology to meet the need of different characteristics of flow. By visualizing the data from 4over6 project and compared with the methods currently used in the project, the results show that spiral visualization can excavate the time characteristics of traffic data more effectively than line mapping; dimension classification is more effective for special dimensions visualization than parallel coordinates and space filling can express node information with no loss of structure characteristics which node-link mapping can＇ t achieve.
出处
《计算机科学与探索》
CSCD
北大核心
2015年第4期451-461,共11页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金Grant No.61161140454
国家高技术研究发展计划(863计划)Grant No.2011AA01A101
高等学校博士学科点专项科研基金Grant No.20120002110060~~
