Predictive visual analytics of event evolution for user-created context

Yeon, Hanbyul; Kim, Seokyeon; Jang, Yun

doi:10.1007/s12650-016-0373-7

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…During the process of investigating research works, we found that the range of pertinent papers is relatively narrow. To solve the potential shortage in the number of references, our survey range covered publications that incorporate anomaly detection as one of their visual analytic approaches in addition to those that solely address the issue of anomaly detection, e.g., we include [15] in our collection through the authors' ultimate goal is predictive analysis of event evolution.…”

Section: B Methodologymentioning

confidence: 99%

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Visual Analytics of Anomalous User Behaviors: A Survey

Shi

Liu

Tong

et al. 2020

IEEE Trans. Big Data

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The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behaviorrelated data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss findings and potential research directions.• We categorize four user behaviors, including social interaction, travel, network communication, and transaction based on the data collected from specific data sources. We extract four common data types from these four behaviors, including text, network, spatiotemporal information, and multidimensional data.• We review how research works use visualization techniques combined with interaction methods to analyze anomalous user behaviors. We extract six

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Section: B Methodologymentioning

confidence: 99%

“…This novel method was first used in ScatterBlogs [43]. It was later applied to identify unusual topics in the selected regions [47] and used as part of predictive analytics based on topic trends in historic time series [15].…”

Section: B Anomaly Detection Techniquesmentioning

confidence: 99%

Visual Analytics of Anomalous User Behaviors: A Survey

Shi

Liu

Tong

et al. 2020

IEEE Trans. Big Data

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“…Yeon et al [YKJ16] covered similar ground in their identification and analysis of interesting past abnormal events as a precursor for predicting future events. Here, as in Lu et al and in other papers using ML, context and analytic power is obtained from combining multiple sources (in this case social media and news media).…”

Section: Streaming Data: Finance Cyber Security Social Mediamentioning

confidence: 95%

“…Yeon et al . [YKJ16] covered similar ground in their identification and analysis of interesting past abnormal events as a precursor for predicting future events. Here, as in Lu et al .…”

Section: Application Domainsmentioning

confidence: 99%

The State of the Art in Integrating Machine Learning into Visual Analytics

Endert

Ribarsky

Turkay

et al. 2017

Computer Graphics Forum

180

122

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Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-ofthe-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

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Text visualization for geological hazard documents via text mining and natural language processing

Ma¹,

Xie

et al. 2022

Earth Sci Inform

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Predictive visual analytics of event evolution for user-created context

Cited by 6 publications

References 35 publications

Visual Analytics of Anomalous User Behaviors: A Survey

Visual Analytics of Anomalous User Behaviors: A Survey

The State of the Art in Integrating Machine Learning into Visual Analytics

Text visualization for geological hazard documents via text mining and natural language processing

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