Skip to main content
SpringerLink
Log in

Recent progress and trends in predictive visual analytics

  • Review Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Larose D T, Larose C D. Data Mining and Predictive Analytics, 2nd ed. Hoboken: John Wiley & Sons, 2015

    MATH  Google Scholar 

  2. Schlangenstein M. UPS crunches data to make more routes more efficient, save gas. http://www.bloomberg.com/news/articles/2013-10-30/ups-uses-big-data-to-make-routes-more-efficient-save-gas, 2013

    Google Scholar 

  3. Ginsberg J, MohebbiMH, Patel R S, Brammer L, SmolinskiMS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature, 2009, 457(7232): 1012–1014

    Article  Google Scholar 

  4. Butler D. When Google got flu wrong. Nature, 2013, 494(7436): 155–156

    Article  Google Scholar 

  5. Culotta A. Towards detecting influenza epidemics by analyzing Twitter messages. In: Proceedings of the 1st Workshop on Social Media Analytics. 2010, 115–122

    Chapter  Google Scholar 

  6. Lazer D, Kennedy R, King G, Vespignani A. The parable of Google flu: traps in big data analysis. Science, 2014, 343(6176): 1203–1205

    Article  Google Scholar 

  7. Keim D A, Kohlhammer J, Ellis G, Mansmann F. Mastering the Information Age — Solving Problems with Visual Analytics. Goslar: Florian Mansmann, 2010

    Google Scholar 

  8. Bertini E, Lalanne D. Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration. 2009, 12–20

    Google Scholar 

  9. Sacha D, Stoffel A, Stoffel F, Kwon B C, Ellis G, Keim D. Knowledge generation model for visual analytics. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1604–1613

    Article  Google Scholar 

  10. El-Assady M, Jentner W, Stein M, Fischer F, Schreck T, Keim D. Predictive visual analytics —approaches for movie ratings and discussion of open research challenges. In: Proceedings of IEEE VIS Workshop: Visualization for Predictive Analytics. 2014

    Google Scholar 

  11. Krause J, Perer A, Bertini E. INFUSE: interactive feature selection for predictive modeling of high dimensional data. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1614–1623

    Article  Google Scholar 

  12. Gleicher M. Position paper: towards comprehensible predictive modeling. In: Proceedings of IEEE VIS Workshop: Visualization for Predictive Analytics. 2014

    Google Scholar 

  13. Kandel S, Paepcke A, Hellerstein J, Heer J. Wrangler: interactive visual specification of data transformation scripts. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011, 3363–3372

    Google Scholar 

  14. Rahm E, Do H H. Data cleaning: problems and current approaches. IEEE Data Eng. Bull., 2000, 23(4): 3–13

    Google Scholar 

  15. Kim W, Choi B J, Hong E K, Kim S K, Lee D. A taxonomy of dirty data. Data Mining and Knowledge Discovery, 2003, 7(1): 81–99

    Article  MathSciNet  Google Scholar 

  16. Ganuza M L, Ferracutti G, Gargiulo M F, Castro S M, Bjerg E, Gröller E, Matković K. The spinel explorer — interactive visual analysis of spinel group minerals. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1913–1922

    Article  Google Scholar 

  17. Brown E T, Ottley A, Zhao H, Lin Q, Souvenir R, Endert A, Chang R. Finding waldo: learning about users from their interactions. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1663–1672

    Article  Google Scholar 

  18. Born S, Sundermann S H, Russ C, Hopf R, Ruiz C E, Falk V, GessatM. Stent maps — comparative visualization for the prediction of adverse events of transcatheter aortic valve implantations. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 2704–2713

    Article  Google Scholar 

  19. Xie C, Chen W, Huang X X, Hu Y Q, Barlowe S, Yang J. VAET: a visual analytics approach for e-transactions time-series. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1743–1752

    Article  Google Scholar 

  20. Madhavan K, Elmqvist N, Vorvoreanu M, Chen X, Wong Y, Xian H, Dong Z, Johri A. Dia2: Web-based cyberinfrastructure for visual analysis of funding portfolios. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1823–1832

    Article  Google Scholar 

  21. Hao M C, Janetzko H, Mittelstädt S, Hill W, Dayal U, Keim D A, Marwah M, Sharma R K. A visual analytics approach for peak-preserving prediction of large seasonal time series. Computer Graphics Forum, 2011, 30(3): 691–700

    Article  Google Scholar 

  22. Hao M C, Marwah M, Janetzko H, Dayal U, Keim D A, Patnaik D, Ramakrishnan N, Sharma R K. Visual exploration of frequent patterns in multivariate time series. Information Visualization, 2012, 11(1): 71–83

    Article  Google Scholar 

  23. Malik A, Maciejewski R, Towers S, McCullough S, Ebert D S. Proactive spatiotemporal resource allocation and predictive visual analytics for community policing and law enforcement. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1863–1872

    Article  Google Scholar 

  24. Hollt T, Magdy A, Zhan P, Chen G, Gopalakrishnan G, Hoteit I, Hansen C D, Hadwiger M. Ovis: a framework for visual analysis of ocean forecast ensembles. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(8): 1114–1126

    Article  Google Scholar 

  25. Doraiswamy H, Ferreira N, Damoulas T, Freire J, Silva C T. Using topological analysis to support event-guided exploration in urban data. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 2634–2643

    Article  Google Scholar 

  26. Chen W, Guo F, Wang F Y. A survey of traffic data visualization. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 2970–2984

    Article  Google Scholar 

  27. Koch S, John M, Worner M, Muller A, Ertl T. Varifocalreader-in-depth visual analysis of large text documents. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1723–1732

    Article  Google Scholar 

  28. Zhao J, Cao N, Wen Z, Song Y, Lin Y R, Collins C M. # FluxFlow: visual analysis of anomalous information spreading on social media. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1773–1782

    Article  Google Scholar 

  29. Sun G, Wu Y, Liu S, Peng T Q, Zhu J J, Liang R. EvoRiver: visual analysis of topic coopetition on social media. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1753–1762

    Article  Google Scholar 

  30. Klemm P, Oeltze-Jafra S, Lawonn K, Hegenscheid K, Volzke H, Preim B. Interactive visual analysis of image-centric cohort study data. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1673–1682

    Article  Google Scholar 

  31. Arietta S M, Efros A, Ramamoorthi R, Agrawala M. City forensics: using visual elements to predict non-visual city attributes. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 2624–2633

    Article  Google Scholar 

  32. Ma Y X, Xu J Y, Peng D C, Zhang T, Jin C Z, Qu HM, ChenW, Peng Q S. A visual analysis approach for community detection of multi-context mobile social networks. Journal of Computer Science and Technology, 2013, 28(5): 797–809

    Article  Google Scholar 

  33. Van den Elzen S, Holten D, Blaas J, Van Wijk J J. Dynamic network visualization with extended massive sequence views. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(8): 1087–1099

    Article  Google Scholar 

  34. Van den Elzen S, Van Wijk J J. Multivariate network exploration and presentation: From detail to overview via selections and aggregations. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 2310–2319

    Article  Google Scholar 

  35. Van den Elzen S, Holten D, Blaas J, Van Wijk J J. Reducing snapshots to points: a visual analytics approach to dynamic network exploration. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 1–10

    Article  Google Scholar 

  36. Gschwandtner T, Gärtner J, Aigner W, Miksch S. A taxonomy of dirty time-oriented data. In: Proceedings of International Conference on Availability, Reliability, and Security. 2012, 58–72

    Google Scholar 

  37. Eaton C, Plaisant C, Drizd T. Visualizing missing data: graph interpretation user study. In: Proceedings of IFIP Conference on HumanComputer Interaction. 2005, 861–872

    Google Scholar 

  38. Templ M, Alfons A, Filzmoser P. Exploring incomplete data using visualization techniques. Advances in Data Analysis and Classification, 2012, 6(1): 29–47

    Article  MathSciNet  Google Scholar 

  39. Lin J, Wong J, Nichols J, Cypher A, Lau T A. End-user programming of mashups with vegemite. In: Proceedings of the 14th International Conference on Intelligent User Interfaces. 2009, 97–106

    Google Scholar 

  40. Scaffidi C, Myers B, Shaw M. Intelligently creating and recommending reusable reformatting rules. In: Proceedings of the 14th International Conference on Intelligent User Interfaces. 2009, 297–306

    Google Scholar 

  41. Ives Z, Knoblock C, Minton S, Jacob M, Talukdar P, Tuchinda R, Ambite J L, Muslea M, Gazen C. Interactive data integration through smart copy & paste. In: Proceedings of the Biennial Conference on Innovative Data Systems Research. 2009

    Google Scholar 

  42. Kandel S, Heer J, Plaisant C, Kennedy J, Van Ham F, Riche N H, Weaver C, Lee B, Brodbeck D, Buono P. Research directions in data wrangling: visualizations and transformations for usable and credible data. Information Visualization, 2011, 10(4): 271–288

    Article  Google Scholar 

  43. Robertson G G, Czerwinski M P, Churchill J E. Visualization of mappings between schemas. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2005, 431–439

    Google Scholar 

  44. Altova. Data integration: opportunities, challenges, and altova mapforce. http://www.altova.com/whitepapers/mapforce.pdf, 2014

  45. Informatica. The informatica data quality methodology: a framework to achieve pervasive data quality through enhanced businessit collaboration. https://www.informatica.com/downloads/7130-DQMethodology-wp-web.pdf, 2010

  46. Zheng Y. Methodologies for cross-domain data fusion: an overview. IEEE Transactions on Big Data, 2015, 1(1): 16–34

    Article  Google Scholar 

  47. Dash M, Liu H. Feature selection for classification. Intelligent Data Analysis, 1997, 1(3): 131–156

    Article  Google Scholar 

  48. Fogarty J, Hudson S E. Toolkit support for developing and deploying sensor-based statistical models of human situations. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2007, 135–144

    Chapter  Google Scholar 

  49. Markovitch S, Rosenstein D. Feature generation using general constructor functions. Machine Learning, 2002, 49(1): 59–98

    Article  MATH  Google Scholar 

  50. Schuller B, Reiter S, Rigoll G. Evolutionary feature generation in speech emotion recognition. In: Proceedings of the IEEE International Conference on Multimedia and Expo. 2006, 5–8

    Google Scholar 

  51. Guo D S. Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Information Visualization, 2003, 2(4): 232–246

    Article  Google Scholar 

  52. Seo J, Shneiderman B. A rank-by-feature framework for unsupervised multidimensional data exploration using low dimensional projections. In: Proceedings of the IEEE Symposium on Information Visualization. 2004, 65–72

    Google Scholar 

  53. Piringer H, Berger W, Hauser H. Quantifying and comparing features in high-dimensional datasets. In: Proceedings of the 12th International Conference on Information Visualization. 2008, 240–245

    Google Scholar 

  54. May T, Bannach A, Davey J, Ruppert T, Kohlhammer J. Guiding feature subset selection with an interactive visualization. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology. 2011, 111–120

    Google Scholar 

  55. Kohavi R, John G H. Wrappers for feature subset selection. Artificial Intelligence, 1997, 97(1): 273–324

    Article  MATH  Google Scholar 

  56. Klemm P, Lawonn K, Glaßer S, Niemann U, Hegenscheid K, Völzke H, Preim B. 3D regression heat map analysis of population study data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 81–90

    Article  Google Scholar 

  57. Lu Y, Wang F, Maciejewski R. Business intelligence from social media: a study from the vast box office challenge. IEEE Computer Graphics and Applications, 2014, 34(5): 58–69

    Article  Google Scholar 

  58. Brooks M, Amershi S, Lee B, Drucker S M, Kapoor A, Simard P. Featureinsight: visual support for error-driven feature ideation in text classification. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology. 2015, 105–112

    Google Scholar 

  59. Bögl M, Aigner W, Filzmoser P, Lammarsch T, Miksch S, Rind A. Visual analytics for model selection in time series analysis. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 2237–2246

    Article  Google Scholar 

  60. Lu Y, Kruger R, Thom D, Wang F, Koch S, Ertl T, Maciejewski R. Integrating predictive analytics and social media. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology. 2014, 193–202

    Google Scholar 

  61. Piringer H, Berger W, Krasser J. Hypermoval: Interactive visual validation of regression models for real-time simulation. Computer Graphics Forum, 2010, 29(3): 983–992

    Article  Google Scholar 

  62. Mühlbacher T, Piringer H. A partition-based framework for building and validating regression models. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 1962–1971

    Article  Google Scholar 

  63. Gotz D, Sun J. Visualizing accuracy to improve predictive model performance. In: Proceedings of the IEEE VISWorkshop on Visualization for Predictive Analytics. 2014

    Google Scholar 

  64. Quinlan J R. Induction of decision trees. Machine Learning, 1986, 1(1): 81–106

    Google Scholar 

  65. Suykens J A, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293–300

    Article  MATH  Google Scholar 

  66. Johnson B, Shneiderman B. Tree-maps: a space-filling approach to the visualization of hierarchical information structures. In: Proceedings of the IEEE Conference on Visualization. 1991, 284–291

    Google Scholar 

  67. Stasko J, Zhang E. Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. In: Proceedings of the IEEE Symposium on Information Visualization. 2000, 57–65

    Google Scholar 

  68. Ware M, Frank E, Holmes G, Hall M, Witten I H. Interactive machine learning: letting users build classifiers. International Journal of Human-Computer Studies, 2001, 55(3): 281–292

    Article  MATH  Google Scholar 

  69. Ankerst M, Elsen C, Ester M, Kriegel H P. Visual classification: an interactive approach to decision tree construction. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1999, 392–396

    Google Scholar 

  70. Van den Elzen S, Van Wijk J J. Baobabview: Interactive construction and analysis of decision trees. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology. 2011, 151–160

    Google Scholar 

  71. Becker B, Kohavi R, Sommerfield D. Visualizing the simple Baysian classifier. In: Fayyad U, Grinstein G G, Wierse A, eds. Information Visualization in Data Mining and Knowledge Discovery. San Francisco: Morgan Kaufmann Publishers Inc., 2002

    Google Scholar 

  72. Caragea D, Cook D, Honavar V G. Gaining insights into support vector machine pattern classifiers using projection-based tour methods. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2001, 251–256

    Google Scholar 

  73. Ma Y. Easy SVM: a visual analysis approach for open-box support vector machines. In: Proceedings of the IEEE VIS Workshop on Visualization for Predictive Analytics. 2014

    Google Scholar 

  74. John G H, Langley P. Estimating continuous distributions in bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in artificial intelligence. 1995, 338–345

    Google Scholar 

  75. Ho T K. Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition. 1995, 278–282

    Google Scholar 

  76. Mühlbacher T, Piringer H, Gratzl S, Sedlmair M, Streit M. Opening the black box: strategies for increased user involvement in existing algorithm implementations. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1643–1652

    Article  Google Scholar 

  77. Paiva J G S, Schwartz W R, Pedrini H, Minghim R. An approach to supporting incremental visual data classification. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(1): 4–17

    Article  Google Scholar 

  78. Talbot J, Lee B, Kapoor A, Tan D S. EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2009, 1283–1292

    Google Scholar 

  79. Wu Y, Pitipornvivat N, Zhao J, Yang S, Huang G, Qu H. egoSlider: visual analysis of egocentric network evolution. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 260–269

    Article  Google Scholar 

  80. Stolper C D, Perer A, Gotz D. Progressive visual analytics: user-driven visual exploration of in-progress analytics. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1653–1662

    Article  Google Scholar 

  81. Ng K, Ghoting A, Steinhubl S R, Stewart W F, Malin B, Sun J. PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records. Journal of Biomedical Informatics, 2014, 48: 160–170

    Article  Google Scholar 

  82. Chang C C, Lin C J. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27

    Article  Google Scholar 

  83. Bögl M, Aigner W, Filzmoser P, Gschwandtner T, Lammarsch T, Miksch S, Rind A. Visual analytics methods to guide diagnostics for time series model predictions. In: Proceedings of the IEEE VIS Workshop on Visualization for Predictive Analytics. 2014

    Google Scholar 

  84. Andrienko N, Andrienko G, Rinzivillo S. Experiences from supporting predictive analytics of vehicle traffic. In: Proceedings of the IEEE VIS Workshop on Visualization for Predictive Analytics. 2014

    Google Scholar 

  85. Maciejewski R, Hafen R, Rudolph S, Larew S G, Mitchell M, Cleveland W S, Ebert D S. Forecasting hotspots — a predictive analytics approach. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(4): 440–453

    Article  Google Scholar 

  86. Cleveland R B, Cleveland W S, McRae J E, Terpenning I. STL: a seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 1990, 6(1): 3–73

    Google Scholar 

  87. Bryan C, Wu X, Mniszewski S, Ma K L. Integrating predictive analytics into a spatiotemporal epidemic simulation. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology. 2015, 17–24

    Google Scholar 

  88. Chuang J, Socher R. Interactive visualizations for deep learning. In: Proceedings of the IEEE VIS Workshop on Visualization for Predictive Analytics. 2014

    Google Scholar 

  89. Yeon H, Jang Y. Predictive visual analytics using topic composition. In: Proceedings of the 8th International Symposium on Visual Information Communication and Interaction. 2015, 1–8

    Google Scholar 

  90. Wu Y C, Liu S X, Yan K, Liu M C, Wu F Z. OpinionFlow: visual analysis of opinion diffusion on social media. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 1763–1772

    Article  Google Scholar 

  91. Choo J, Lee H, Kihm J, Park H. iVisClassifier: an interactive visual analytics system for classification based on supervised dimension reduction. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology. 2010, 27–34

    Google Scholar 

  92. Höferlin B, Netzel R, Höferlin M, Weiskopf D, Heidemann G. Interactive learning of ad-hoc classifiers for video visual analytics. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology. 2012, 23–32

    Google Scholar 

  93. Heimerl F, Koch S, Bosch H, Ertl T. Visual classifier training for text document retrieval. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(12): 2839–2848

    Article  Google Scholar 

  94. Munzner T. Visualization Analysis and Design. Boca Raton: CRC Press, 2014

    Google Scholar 

  95. Delevingne L. Hedge fund robots crushed human rivals in 2014. http://www.cnbc.com/2015/01/05/hedge-fund-robots-crushed-humanrivals-in-2014.html, 2015

    Google Scholar 

  96. Seifert M, Hadida A L. On the relative importance of linear model and human judge(s) in combined forecasting. Organizational Behavior and Human Decision Processes, 2013, 120(1): 24–36

    Article  Google Scholar 

  97. Ruchikachorn P, Mueller K. Learning visualizations by analogy: promoting visual literacy through visualization morphing. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(9): 1028–1044

    Article  Google Scholar 

  98. Amini F, Rufiange S, Hossain Z, Ventura Q, Irani P, McGuffin MJ. The impact of interactivity on comprehending 2D and 3D visualizations of movement data. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(1): 122–135

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Basic Research Program of China (973 Program) (2015CB352503), Major Program of the National Natural Science Foundation of China (61232012), the National Natural Science Foundation of China (Grant Nos. 61303141, 61422211, u1536118, u1536119), Zhejiang Provincial Natural Science Foundation of China (LR13F020001), the Fundamental Research Funds for the Central Universities, the Innovation Joint Research Center for Cyber-Physical-Society System, and the United State’s National Science Foundation (1350573).

Author information

Authors and Affiliations

  1. State Key Lab of Computer Aided Design and Computer Graphics, Zhejiang University, Hangzhou, 310058, China

    Junhua Lu, Wei Chen, Yuxin Ma & Zongzhuang Li

  2. College of Science, Zhejiang University of Technology, Hangzhou, 310023, China

    Junming Ke

  3. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Fan Zhang

  4. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe AZ, 85287-8809, USA

    Ross Maciejewski

Authors
  1. Junhua Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuxin Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junming Ke
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zongzhuang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ross Maciejewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Chen.

Additional information

Junhua Lu is currently working toward the PhD degree with the State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, China. His research interests include visualization and visual analytics.

Wei Chen is currently a professor at the State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, China. He has published more than 60 papers in international journals and conferences. Prof. Chen served as a steering committee member of the IEEE Pacific Visualization, the conference chair of the IEEE Pacific Visualization 2015, and a paper cochair of the IEEE Pacific Visualization 2014. He is an awardee of the NSFC Excellent Young Scholars Program in 2014.

Yuxin Ma is currently working toward the PhD degree with the State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, China. His research interests include visual analytics and visual data mining.

Junming Ke is an undergraduate student of Zhejiang University of Technology, China. He is undergoing an internship in the State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, China.

Zongzhuang Li is an undergraduate student of Zhejiang University (ZJU), China. He is working on his graduation proposal in the State Key Laboratory of Computer Aided Design and Computer Graphics, ZJU.

Fan Zhang is currently an associate professor at the Zhejiang University of Technology, China. His research interests include visual analytics and parallel computing.

Ross Maciejewski is an assistant professor in the School of Computing, Informatics & Decision Systems Engineering, Arizona State University, USA. His primary research interests are in the areas of geographical visualization and visual analytics focusing on public health, dietary analysis, social media, and criminal incident reports. He has served on the organizing committee for the IEEE Conference on Visual Analytics Science and Technology (2012-2013, 2015) and the IEEE/VGTC EuroVis Conference (2014-2016) and has been involved in award winning submissions to the IEEE Visual Analytics Contest (2010, 2013, 2015). He is also a fellow of the Global Security Initiative at ASU and the recipient of an NSF CAREER Award (2014).

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, J., Chen, W., Ma, Y. et al. Recent progress and trends in predictive visual analytics. Front. Comput. Sci. 11, 192–207 (2017). https://doi.org/10.1007/s11704-016-6028-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-016-6028-y

Keywords

Search

Navigation