logo

SCIENCE CHINA Information Sciences, Volume 60, Issue 12: 121101(2017) https://doi.org/10.1007/s11432-016-0428-2

A survey of network anomaly visualization

More info
  • ReceivedJul 7, 2016
  • AcceptedAug 12, 2016
  • PublishedApr 24, 2017

Abstract

Network anomaly analysis is an emerging subtopic of network security. Network anomaly refers to the unusual behavior of network devices or suspicious network status. A number of intelligent visual tools are developed to enhance the ability of network security analysts in understanding the original data, ultimately solving network security problems. This paper surveys current progress and trends in network anomaly visualization. By providing an overview of network anomaly data, visualization tasks, and applications, we further elaborate on existing methods to depict various data features of network alerts, anomalous traffic, and attack patterns data. Directions for future studies are outlined at the end of this paper.


Acknowledgment

This work was supported by National Basic Research Program of China (973 Program) (Grant No. 2015CB352503), Major Program of National Natural Science Foundation of China (Grant No. 61232012), National Natural Science Foundation of China (Grant Nos. 61422211, u1536118, u1536119), Zhejiang Provincial Natural Science Foundation of China (Grant No. LR13F020001), and Fundamental Research Funds for the Central Universities.


References

[1] Shiravi H, Shiravi A, Ghorbani A A. A survey of visualization systems for network security.. IEEE Trans Visual Comput Graphics, 2012, 18: 1313-1329 CrossRef PubMed Google Scholar

[2] Pearlman J, Rheingans P. Visualizing network security events using compound glyphs from a service-oriented perspective. In: Proceedings of the Workshop on Visualization for Computer Security, Sacramento, 2008. 131--146. Google Scholar

[3] Janies J. Existence plots: a low-resolution time series for port behavior analysis. In: Proceedings of the 5th International Workshop on Visualization for Computer Security, Cambridge, 2008. 161--168. Google Scholar

[4] Koike H, Ohno K. SnortView: visualization system of snort logs. In: Proceedings of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security, Washington, 2004. 143--147. Google Scholar

[5] Bertini E, Hertzog P, Lalanne D. Spiralview: towards security policies assessment through visual correlation of network resources with evolution of alarms. In: Proceedings of IEEE Symposium on Visual Analytics Science and Technology, Washington, 2007. 139--146. Google Scholar

[6] Foresti S, Agutter J, Livnat Y, et al. Visual correlation of network alerts. IEEE Comput Graph, 2006, 26: 48--59. Google Scholar

[7] Lee C P, Tros J, Gibbs N, et al. Visual firewall: real-time network security monitor. In: Proceedings of IEEE Workshop on Visualization for Computer Security, Minneapolis, 2005. 129--136. Google Scholar

[8] Koike H, Ohno K, Koizumi K. Visualizing cyber attacks using ip matrix. In: Proceedings of IEEE Workshop on Visualization for Computer Security, Minneapolis, 2005. 91--98. Google Scholar

[9] Lamagna W M. An integrated visualization on network events vast 2011 mini challenge #2 award: outstanding integrated overview display. In: Proceedings of IEEE Conference on Visual Analytics Science and Technology, Providence, 2011. 319--321. Google Scholar

[10] Giacobe N A, Xu S. Geovisual analytics for cyber security: adopting the geoviz toolkit. In: Proceedings of IEEE Conference on Visual Analytics Science and Technology, Providence, 2011. 315--316. Google Scholar

[11] Shiravi H, Shiravi A, Ghorbani A A. IDS alert visualization and monitoring through heuristic host selection. In: Proceedings of International Conference on Information and Communications Security, Barcelona, 2010. 445--458. Google Scholar

[12] Erbacher R F. Intrusion behavior detection through visualization. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Washington, 2003. 2507--2513. Google Scholar

[13] Abdullah K, Lee C, Conti G, et al. IDS rainstorm: visualizing IDS alarms. In: Proceedings of the IEEE Workshops on Visualization for Computer Security, Minneapolis, 2005. 1. Google Scholar

[14] Erbacher R F, Walker K L, Frincke D A. Intrusion and misuse detection in large-scale systems. IEEE Comput Graph, 2002, 22: 38--47. Google Scholar

[15] Girardin L. An eye on network intruder-administrator shootouts. In: Proceedings of Workshop on Intrusion Detection and Network Monitoring, Santa Clara, 1999. 19--28. Google Scholar

[16] Nyarko K, Capers T, Scott C, et al. Network intrusion visualization with niva, an intrusion detection visual analyzer with haptic integration. In: Proceedings of 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, Orlando, 2002. 277--284. Google Scholar

[17] Maltego. Paterva Company. http://www.paterva.com/web7. Google Scholar

[18] Wong T, Jacobson V, Alaettinoglu C. Internet routing anomaly detection and visualization. In: Proceedings of International Conference on Dependable Systems and Networks, Yokohama, 2005. 172--181. Google Scholar

[19] Teoh S T, Zhang K, Tseng S M, et al. Combining visual and automated data mining for near-real-time anomaly detection and analysis in BGP. In: Proceedings of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security, Washington, 2004. 35--44. Google Scholar

[20] Teoh S T, Ranjan S, Nucci A, et al. BGP eye: a new visualization tool for real-time detection and analysis of BGP anomalies. In: Proceedings of the 3rd International Workshop on Visualization for Computer Security, Alexandria, 2006. 81--90. Google Scholar

[21] Arendt D L, Burtner R, Best D M, et al. Ocelot: user-centered design of a decision support visualization for network quarantine. In: Proceedings of IEEE Symposium on Visualization for Cyber Security, Chicago, 2015. 1--8. Google Scholar

[22] Takada T, Koike H. Tudumi: information visualization system for monitoring and auditing computer logs. In: Proceedings fo 6th International Conference on Information Visualisation, London, 2002. 570--576. Google Scholar

[23] Ren P, Kristoff J, Gooch B. Visualizing DNS traffic. In: Proceedings of the 3rd International Workshop on Visualization for Computer Security, Alexandria, 2006. 23--30. Google Scholar

[24] Goodall J R, Lutters W G, Rheingans P, et al. Preserving the big picture: visual network traffic analysis with TN. łinebreak In: Proceedings of IEEE Workshop on Visualization for Computer Security, Minneapolis, 2005. 47--54. Google Scholar

[25] Yin X X, Yurcik W, Treaster M, et al. Visflowconnect: netflow visualizations of link relationships for security situational awareness. In: Proceedings of ACM Workshop on Visualization and Data Mining for Computer Security, Washington, 2004. 26--34. Google Scholar

[26] [Front cover]. IEEE Comput Grap Appl, 2015, 35: c1-c1 CrossRef Google Scholar

[27] Onut I V, Ghorbani A A. SVision: A novel visual network-anomaly identification technique. Comp Security, 2007, 26: 201-212 CrossRef Google Scholar

[28] Ball R, Fink G A, North C. Home-centric visualization of network traffic for security administration. In: Proceedings of ACM Workshop on Visualization and Data Mining for Computer Security, Washington, 2004. 55--64. Google Scholar

[29] Lakkaraju K, Yurcik W, Lee A J. Nvisionip: netflow visualizations of system state for security situational awareness. In: Proceedings of ACM Workshop on Visualization and Data Mining for Computer Security, Washington, 2004. 65--72. Google Scholar

[30] Keim D A, Mansmann F, Schneidewind J, et al. Monitoring network traffic with radial traffic analyzer. In: Proceedings of IEEE Symposium on Visual Analytics Science and Technology, Baltimore, 2006. 123--128. Google Scholar

[31] Hao L H, Healey C G, Hutchinson S E. Ensemble visualization for cyber situation awareness of network security data. In: Proceedings of IEEE Symposium on Visualization for Cyber Security, Chicago, 2015. 1--8. Google Scholar

[32] [Front cover]. IBM J Res Dev, 2013, 57: C1-C1 CrossRef Google Scholar

[33] Fink G A, Muessig P, North C. Visual correlation of host processes and network traffic. In: Proceedings of IEEE Workshop on Visualization for Computer Security, Minneapolis, 2005. 11--19. Google Scholar

[34] Ren P, Gao Y, Li Z, et al. Idgraphs: intrusion detection and analysis using histographs. In: Proceedings of IEEE Workshop on Visualization for Computer Security, Minneapolis, 2005. 39--46. Google Scholar

[35] McPherson J, Ma K L, Krystosk P, et al. Portvis: a tool for port-based detection of security events. In: Proceedings of ACM Workshop on Visualization and Data Mining for Computer Security, Washington, 2004. 73--81. Google Scholar

[36] Abdullah K, Lee C, Conti G, et al. Visualizing network data for intrusion detection. In: Proceedings of Information Assurance Workshop From the 6th Annual IEEE SMC, College Park, 2005. 100--108. Google Scholar

[37] Taylor T, Paterson D, Glanfield J, et al. Flovis: flow visualization system. In: Proceedings of the Cybersecurity Applications & Technology Conference for Homeland Security, Washington, 2009. 186--198. Google Scholar

[38] Glanfield J, Brooks S, Taylor T, et al. Over flow: an overview visualization for network analysis. In: Proceedings of the International Workshop on Visualization for Cyber Security, Atlantic, 2009. 11--19. Google Scholar

[39] Zhao Y, Liang X, Fan X. MVSec: multi-perspective and deductive visual analytics on heterogeneous network security data. J Vis, 2014, 17: 181-196 CrossRef Google Scholar

[40] Fischer F, Mansmann F, Keim D A, et al. Large-scale network monitoring for visual analysis of attacks. In: Proceedings of the 5th International Workshop on Visualization for Computer Security, Cambridge, 2008. 111--118. Google Scholar

[41] Cortese P F, Di Battista G, Moneta A. Topographic visualization of prefix propagation in the internet.. IEEE Trans Visual Comput Graphics, 2006, 12: 725-732 CrossRef PubMed Google Scholar

[42] Mansmann F, Keim D A, North S C. Visual analysis of network traffic for resource planning, interactive monitoring, and interpretation of security threats.. IEEE Trans Visual Comput Graphics, 2007, 13: 1105-1112 CrossRef PubMed Google Scholar

[43] Inoue D, Eto M, Suzuki K, et al. Daedalus-viz: novel real-time 3D visualization for darknet monitoring-based alert system. In: Proceedings of the 9th International Symposium on Visualization for Cyber Security, Seattle, 2012. 72--79. Google Scholar

[44] Inoue D, Eto M, Yoshioka K, et al. Nicter: an incident analysis system toward binding network monitoring with malware analysis. In: Proceedings of WOMBAT Workshop on Information Security Threats Data Collection and Sharing, Amsterdam, 2008. 58--66. Google Scholar

[45] Oberheide J, Goff M, Karir M. Flamingo: visualizing internet traffic. In: Proceedings of Network Operations and Management Symposium, Vancouver, 2006. 150--161. Google Scholar

[46] Yelizarov A, Gamayunov D. Visualization of complex attacks and state of attacked network. In: Proceedings of VizSec International Workshop on Visualization for Cyber Security, Atlantic, 2009. 1--9. Google Scholar

[47] Angelini M, Prigent N, Santucci G. Percival: proactive and reactive attack and response assessment for cyber incidents using visual analytics. In: Proceedings of IEEE Symposium on Visualization for Cyber Security, Chicago, 2015. 1--8. Google Scholar

[48] Becker R A, Eick S G, Wilks A R. Visualizing network data. IEEE Trans Visual Comput Graphics, 1995, 1: 16-28 CrossRef Google Scholar

[49] Matuszak W J, DiPippo L, Sun Y L. Cybersave: situational awareness visualization for cyber security of smart grid systems. In: Proceedings of the 10th Workshop on Visualization for Cyber Security, Atlanta, 2013. 25--32. Google Scholar

[50] Kotenko I, Novikova E. Visualization of security metrics for cyber situation awareness. In: Proceedings of International Conference on Availability, Reliability and Security, Switzerland, 2014. 506--513. Google Scholar

[51] Zhao Y, Fan X P, Zhou F F, et al. A survey on network security data visualization. J Comput Aided Des Comput Graph, 2014, 26: 687--697. Google Scholar

[52] Zhuo W, Nadjin Y. Malwarevis: entity-based visualization of malware network traces. In: Proceedings of the 9th International Symposium on Visualization for Cyber Security, Seattle, 2012. 41--47. Google Scholar

[53] Trinius P, Holz T, Gobel J, et al. Visual analysis of malware behavior using treemaps and thread graphs. In: Proceedings of 6th International Workshop on Visualization for Cyber Security, Atlantic, 2009. 33--38. Google Scholar

[54] Gove R, Saxe J, Gold S, et al. Seem: a scalable visualization for comparing multiple large sets of attributes for malware analysis. In: Proceedings of the Eleventh Workshop on Visualization for Cyber Security, Paris, 2014. 72--79. Google Scholar

[55] Erbacher R F, Christensen K, Sundberg A. Designing visualization capabilities for IDS challenges. In: Proceedings of IEEE Workshop on Visualization for Computer Security, Minneapolis, 2005. 121--127. Google Scholar

[56] Card S K, Mackinlay J D, Shneiderman B. Readings in Information Visualization: Using Vision to Think. San Francisco: Morgan Kaufmann, 1999. Google Scholar

[57] Aigner W, Miksch S, Muller W. Visual methods for analyzing time-oriented data.. IEEE Trans Visual Comput Graphics, 2008, 14: 47-60 CrossRef PubMed Google Scholar

[58] Xie C, Chen W, Huang X. VAET: A Visual Analytics Approach for E-Transactions Time-Series.. IEEE Trans Visual Comput Graphics, 2014, 20: 1743-1752 CrossRef PubMed Google Scholar

[59] Kondo B, Collins C. DimpVis: Exploring Time-varying Information Visualizations by Direct Manipulation.. IEEE Trans Visual Comput Graphics, 2014, 20: 2003-2012 CrossRef PubMed Google Scholar

[60] Isaacs K E, Bremer P T, Jusufi I. Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time.. IEEE Trans Visual Comput Graphics, 2014, 20: 2349-2358 CrossRef PubMed Google Scholar

[61] Gotz D, Stavropoulos H. DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data.. IEEE Trans Visual Comput Graphics, 2014, 20: 1783-1792 CrossRef PubMed Google Scholar

[62] Cho I, Dou W, Wang D X. VAiRoma: A Visual Analytics System for Making Sense of Places, Times, and Events in Roman History.. IEEE Trans Visual Comput Graphics, 2016, 22: 210-219 CrossRef PubMed Google Scholar

[63] Fulda J, Brehmel M, Munzner T. TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text.. IEEE Trans Visual Comput Graphics, 2016, 22: 300-309 CrossRef PubMed Google Scholar

[64] Loorak M H, Perin C, Kamal N. TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients.. IEEE Trans Visual Comput Graphics, 2016, 22: 409-418 CrossRef PubMed Google Scholar

[65] Walker J, Borgo R, Jones M W. TimeNotes: A Study on Effective Chart Visualization and Interaction Techniques for Time-Series Data.. IEEE Trans Visual Comput Graphics, 2016, 22: 549-558 CrossRef PubMed Google Scholar

[66] Bach B, Shi C, Heulot N. Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data.. IEEE Trans Visual Comput Graphics, 2016, 22: 559-568 CrossRef PubMed Google Scholar

[67] Gu Y, Wang C, Peterka T. Mining Graphs for Understanding Time-Varying Volumetric Data.. IEEE Trans Visual Comput Graphics, 2016, 22: 965-974 CrossRef PubMed Google Scholar

[68] Albo Y, Lanir J, Bak P. Off the Radar: Comparative Evaluation of Radial Visualization Solutions for Composite Indicators.. IEEE Trans Visual Comput Graphics, 2016, 22: 569-578 CrossRef PubMed Google Scholar

[69] Gschwandtner T, Bogl M, Federico P. Visual Encodings of Temporal Uncertainty: A Comparative User Study.. IEEE Trans Visual Comput Graphics, 2016, 22: 539-548 CrossRef PubMed Google Scholar

[70] Sun G, Wu Y, Liu S. EvoRiver: Visual Analysis of Topic Coopetition on Social Media.. IEEE Trans Visual Comput Graphics, 2014, 20: 1753-1762 CrossRef PubMed Google Scholar

[71] Heimerl F, Han Q, Koch S. CiteRivers: Visual Analytics of Citation Patterns.. IEEE Trans Visual Comput Graphics, 2016, 22: 190-199 CrossRef PubMed Google Scholar

[72] Zhao J, Cao N, Wen Z. FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media.. IEEE Trans Visual Comput Graphics, 2014, 20: 1773-1782 CrossRef PubMed Google Scholar

[73] Chen W, Guo F, Wang F Y. A Survey of Traffic Data Visualization. IEEE Trans Intell Transport Syst, 2015, 16: 2970-2984 CrossRef Google Scholar

[74] Gratzl S, Gehlenborg N, Lex A. Domino: Extracting, Comparing, and Manipulating Subsets Across Multiple Tabular Datasets.. IEEE Trans Visual Comput Graphics, 2014, 20: 2023-2032 CrossRef PubMed Google Scholar

[75] Kim H, Choo J, Park H. InterAxis: Steering Scatterplot Axes via Observation-Level Interaction.. IEEE Trans Visual Comput Graphics, 2016, 22: 131-140 CrossRef PubMed Google Scholar

[76] Lowe T, Forster E C, Albuquerque G. Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes.. IEEE Trans Visual Comput Graphics, 2016, 22: 151-159 CrossRef PubMed Google Scholar

[77] Chen W, Shen Z Q, Tao Y B. Data Visualization. Beijing: Publishing House of Electronic Industry, 2013. Google Scholar

[78] Cao N, Shi C, Lin S. TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems.. IEEE Trans Visual Comput Graphics, 2016, 22: 280-289 CrossRef PubMed Google Scholar

[79] Rubio-Sanchez M, Raya L, Diaz F. A comparative study between RadViz and Star Coordinates.. IEEE Trans Visual Comput Graphics, 2016, 22: 619-628 CrossRef PubMed Google Scholar

[80] Papadopoulos C, Gutenko I, Kaufman A E. VEEVVIE: Visual Explorer for Empirical Visualization, VR and Interaction Experiments.. IEEE Trans Visual Comput Graphics, 2016, 22: 111-120 CrossRef PubMed Google Scholar

[81] Wang J, Mueller K. The Visual Causality Analyst: An Interactive Interface for Causal Reasoning.. IEEE Trans Visual Comput Graphics, 2016, 22: 230-239 CrossRef PubMed Google Scholar

[82] Lee S, Kim S H, Hung Y H. How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novices Information Visualization Sensemaking.. IEEE Trans Visual Comput Graphics, 2016, 22: 499-508 CrossRef PubMed Google Scholar

[83] Johansson J, Forsell C. Evaluation of Parallel Coordinates: Overview, Categorization and Guidelines for Future Research.. IEEE Trans Visual Comput Graphics, 2016, 22: 579-588 CrossRef PubMed Google Scholar

[84] Raidou R G, Eisemann M, Breeuwer M. Orientation-Enhanced Parallel Coordinate Plots.. IEEE Trans Visual Comput Graphics, 2016, 22: 589-598 CrossRef PubMed Google Scholar

[85] Chen H, Zhang S, Chen W. Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration.. IEEE Trans Visual Comput Graphics, 2015, 21: 1072-1086 CrossRef PubMed Google Scholar

[86] Roberts J C, Headleand C, Ritsos P D. Sketching Designs Using the Five Design-Sheet Methodology.. IEEE Trans Visual Comput Graphics, 2016, 22: 419-428 CrossRef PubMed Google Scholar

[87] VanderPlas S, Hofmann H. Spatial Reasoning and Data Displays.. IEEE Trans Visual Comput Graphics, 2016, 22: 459-468 CrossRef PubMed Google Scholar

[88] Goodwin S, Dykes J, Slingsby A. Visualizing Multiple Variables Across Scale and Geography.. IEEE Trans Visual Comput Graphics, 2016, 22: 599-608 CrossRef PubMed Google Scholar

[89] Scheepens R, Hurter C, Van De Wetering H. Visualization, Selection, and Analysis of Traffic Flows.. IEEE Trans Visual Comput Graphics, 2016, 22: 379-388 CrossRef PubMed Google Scholar

[90] Lehmann D J, Theisel H. Optimal Sets of Projections of High-Dimensional Data.. IEEE Trans Visual Comput Graphics, 2016, 22: 609-618 CrossRef PubMed Google Scholar

[91] Cheng S, Mueller K. The Data Context Map: Fusing Data and Attributes into a Unified Display.. IEEE Trans Visual Comput Graphics, 2016, 22: 121-130 CrossRef PubMed Google Scholar

[92] Jackle D, Fischer F, Schreck T. Temporal MDS Plots for Analysis of Multivariate Data.. IEEE Trans Visual Comput Graphics, 2016, 22: 141-150 CrossRef PubMed Google Scholar

[93] Stahnke J, Dork M, Muller B. Probing Projections: Interaction Techniques for Interpreting Arrangements and Errors of Dimensionality Reductions.. IEEE Trans Visual Comput Graphics, 2016, 22: 629-638 CrossRef PubMed Google Scholar

[94] Kohonen T. Self-Organizing Maps. New York: Springer, 1997. 266--270. Google Scholar

[95] Amini F, Rufiange S, Hossain Z. The Impact of Interactivity on Comprehending 2D and 3D Visualizations of Movement Data.. IEEE Trans Visual Comput Graphics, 2015, 21: 122-135 CrossRef PubMed Google Scholar

[96] Tory M, Kirkpatrick A E, Atkins M S. Visualization task performance with 2D, 3D, and combination displays.. IEEE Trans Visual Comput Graphics, 2006, 12: 2-13 CrossRef PubMed Google Scholar

[97] Sun M, Mi P, North C. BiSet: Semantic Edge Bundling with Biclusters for Sensemaking.. IEEE Trans Visual Comput Graphics, 2016, 22: 310-319 CrossRef PubMed Google Scholar

[98] von Landesberger T, Brodkorb F, Roskosch P. MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering.. IEEE Trans Visual Comput Graphics, 2016, 22: 11-20 CrossRef PubMed Google Scholar

[99] Krause J, Perer A, Bertini E. INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data.. IEEE Trans Visual Comput Graphics, 2014, 20: 1614-1623 CrossRef PubMed Google Scholar

[100] Mahyar N, Tory M. Supporting Communication and Coordination in Collaborative Sensemaking.. IEEE Trans Visual Comput Graphics, 2014, 20: 1633-1642 CrossRef PubMed Google Scholar

[101] Stolper C D, Perer A, Gotz D. Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics.. IEEE Trans Visual Comput Graphics, 2014, 20: 1653-1662 CrossRef PubMed Google Scholar

[102] Klemm P, Oeltze-Jafra S, Lawonn K. Interactive Visual Analysis of Image-Centric Cohort Study Data.. IEEE Trans Visual Comput Graphics, 2014, 20: 1673-1682 CrossRef PubMed Google Scholar

[103] Jang S, Elmqvist N, Ramani K. MotionFlow: Visual Abstraction and Aggregation of Sequential Patterns in Human Motion Tracking Data.. IEEE Trans Visual Comput Graphics, 2016, 22: 21-30 CrossRef PubMed Google Scholar

[104] Nguyen P H, Xu K, Wheat A. SensePath: Understanding the Sensemaking Process Through Analytic Provenance.. IEEE Trans Visual Comput Graphics, 2016, 22: 41-50 CrossRef PubMed Google Scholar

[105] Blascheck T, John M, Kurzhals K. VA2: A Visual Analytics Approach for // Evaluating Visual Analytics Applications.. IEEE Trans Visual Comput Graphics, 2016, 22: 61-70 CrossRef PubMed Google Scholar

[106] Kwon B C, Kim S H, Lee S. VisOHC: Designing Visual Analytics for Online Health Communities.. IEEE Trans Visual Comput Graphics, 2016, 22: 71-80 CrossRef PubMed Google Scholar

[107] Glueck M, Hamilton P, Chevalier F. PhenoBlocks: Phenotype Comparison Visualizations.. IEEE Trans Visual Comput Graphics, 2016, 22: 101-110 CrossRef PubMed Google Scholar

[108] Guo H, Gomez S R, Ziemkiewicz C. A Case Study Using Visualization Interaction Logs and Insight Metrics to Understand How Analysts Arrive at Insights.. IEEE Trans Visual Comput Graphics, 2016, 22: 51-60 CrossRef PubMed Google Scholar

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1