期刊
IEEE TRANSACTIONS ON BIG DATA
卷 8, 期 2, 页码 377-396出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBDATA.2020.2964169
关键词
Data visualization; Visual analytics; Anomaly detection; Taxonomy; Social networking (online); Spatiotemporal phenomena; Big Data; Anomaly detection; visual analytics; user behaviors
资金
- Fundamental Research Funds for the Central Universities in China
- NSFC [61802283, 61602306]
- NSF [1939725, 1715385, 1947203, 1813464]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1947203] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1715385, 1813464] Funding Source: National Science Foundation
With the increasing use of information technologies and the availability of more data, understanding user behaviors has new opportunities. This article surveys the latest research in visual analytics of anomalous user behaviors and categorizes them into four application domains: social interaction, travel, network communication, and financial transaction. The research work in each category is examined in terms of data types, visualization techniques, and interactive analysis methods. The survey aims to provide researchers and practitioners with systematic guidelines and discusses potential directions for future research in visual analytics of user behaviors.
With the pervasive use of information technologies, the increasing availability of data provides new opportunities for understanding user behaviors. Unearthing anomalies in user behavior is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. In this article, we survey state-of-the-art research work in visual analytics of anomalous user behaviors and classify them into four application domains, which are social interaction, travel, network communication, and financial transaction. We further examine the research work in each category in terms of data types, visualization techniques, and interactive analysis methods. We hope that our survey can provide systematic guidelines for researchers and practitioners to find effective solutions to their research problems in specific application domains. Finally, we discuss trends of academic interest over the past decades and suggest potential directions across visual analytics of these user behaviors for future research.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据