Please wait a minute...

Frontiers of Computer Science

Front. Comput. Sci.    2017, Vol. 11 Issue (2) : 192-207     DOI: 10.1007/s11704-016-6028-y
REVIEW ARTICLE |
Recent progress and trends in predictive visual analytics
Junhua LU1,Wei CHEN1(),Yuxin MA1,Junming KE2,Zongzhuang LI1,Fan ZHANG3,Ross MACIEJEWSKI4
1. State Key Lab of Computer Aided Design and Computer Graphics, Zhejiang University, Hangzhou 310058, China
2. College of Science, Zhejiang University of Technology, Hangzhou 310023, China
3. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
4. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe AZ 85287-8809, USA
Download: PDF(815 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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.

Keywords predictive visual analytics      visualization      visual analytics      data mining      predictive analysis     
Corresponding Authors: Wei CHEN   
Just Accepted Date: 08 June 2016   Online First Date: 31 October 2016    Issue Date: 06 April 2017
 Cite this article:   
Junhua LU,Wei CHEN,Yuxin MA, et al. Recent progress and trends in predictive visual analytics[J]. Front. Comput. Sci., 2017, 11(2): 192-207.
 URL:  
http://journal.hep.com.cn/fcs/EN/10.1007/s11704-016-6028-y
http://journal.hep.com.cn/fcs/EN/Y2017/V11/I2/192
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Junhua LU
Wei CHEN
Yuxin MA
Junming KE
Zongzhuang LI
Fan ZHANG
Ross MACIEJEWSKI
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
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
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
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
doi: 10.1177/1473871611415994
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
doi: 10.1145/1054972.1055032
44 Altova. Data integration: opportunities, challenges, and altova mapforce. , 2014
45 Informatica. The informatica data quality methodology: a framework to achieve pervasive data quality through enhanced businessit collaboration. , 2010
46 Zheng Y. Methodologies for cross-domain data fusion: an overview. IEEE Transactions on Big Data, 2015, 1(1): 16–34
doi: 10.1109/TBDATA.2015.2465959
47 Dash M, Liu H. Feature selection for classification. Intelligent Data Analysis, 1997, 1(3): 131–156
doi: 10.1016/S1088-467X(97)00008-5
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
doi: 10.1145/1240624.1240645
49 Markovitch S, Rosenstein D. Feature generation using general constructor functions. Machine Learning, 2002, 49(1): 59–98
doi: 10.1023/A:1014046307775
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
doi: 10.1109/icme.2006.262500
1 Larose D T, larose C D. Data Mining and Predictive Analytics, 2nd ed. Hoboken: John Wiley & Sons, 2015
2 Schlangenstein M. UPS crunches data to make more routes more efficient, save gas. , 2013
51 Guo D S. Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Information Visualization, 2003, 2(4): 232–246
doi: 10.1057/palgrave.ivs.9500053
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
3 Ginsberg J, Mohebbi M H, Patel R S, Brammer L, Smolinski M S, Brilliant L. Detecting influenza epidemics using search engine query data. Nature, 2009, 457(7232): 1012–1014
doi: 10.1038/nature07634
4 Butler D. When Google got flu wrong. Nature, 2013, 494(7436): 155–156
doi: 10.1038/494155a
5 Culotta A. Towards detecting influenza epidemics by analyzing Twitter messages. In: Proceedings of the 1st Workshop on Social Media Analytics. 2010, 115–122
doi: 10.1145/1964858.1964874
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
doi: 10.1126/science.1248506
7 Keim D A, Kohlhammer J, Ellis G, Mansmann F. Mastering the Information Age — Solving Problems with Visual Analytics. Goslar: Florian Mansmann, 2010
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
doi: 10.1145/1562849.1562851
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
doi: 10.1109/TVCG.2014.2346481
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
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
doi: 10.1109/iv.2008.17
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
doi: 10.1109/vast.2011.6102448
55 Kohavi R, John G H. Wrappers for feature subset selection. Artificial Intelligence, 1997, 97(1): 273–324
doi: 10.1016/S0004-3702(97)00043-X
56 Klemm P, Lawonn K, Glaöer 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
doi: 10.1109/TVCG.2015.2468291
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
doi: 10.1109/MCG.2014.61
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
doi: 10.1109/vast.2015.7347637
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
doi: 10.1109/TVCG.2013.222
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
doi: 10.1109/vast.2014.7042495
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
doi: 10.1109/TVCG.2014.2346482
12 Gleicher M. Position paper: towards comprehensible predictive modeling. In: Proceedings of IEEE VIS Workshop: Visualization for Predictive Analytics. 2014
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
doi: 10.1111/j.1467-8659.2009.01684.x
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
doi: 10.1109/TVCG.2013.125
63 Gotz D, Sun J. Visualizing accuracy to improve predictive model performance. In: Proceedings of the IEEE VISWorkshop on Visualization for Predictive Analytics. 2014
64 Quinlan J R. Induction of decision trees. Machine Learning, 1986, 1(1): 81–106
doi: 10.1007/BF00116251
65 Suykens J A, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293–300
doi: 10.1023/A:1018628609742
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
doi: 10.1109/visual.1991.175815
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
doi: 10.1109/infvis.2000.885091
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
doi: 10.1006/ijhc.2001.0499
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
doi: 10.1145/312129.312298
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
doi: 10.1109/vast.2011.6102453
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
72 Caragea D, Cook D, Honavar V G. Gaining insights into support vec tor 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
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
doi: 10.1145/1978942.1979444
14 Rahm E, Do H H. Data cleaning: problems and current approaches. IEEE Data Eng. Bull., 2000, 23(4): 3–13
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
doi: 10.1023/A:1021564703268
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
doi: 10.1109/TVCG.2014.2346754
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
doi: 10.1109/TVCG.2014.2346575
18 Born S, Sundermann S H, Russ C, Hopf R, Ruiz C E, Falk V, Gessat M. 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
doi: 10.1109/TVCG.2014.2346459
73 Ma Y. EasySVM: a visual analysis approach for open-box support vector machines. In: Proceedings of the IEEE VIS Workshop on Visualization for Predictive Analytics. 2014
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
75 Ho T K. Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition. 1995, 278–282
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
doi: 10.1109/TVCG.2014.2346578
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
doi: 10.1109/TVCG.2014.2331979
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
doi: 10.1145/1518701.1518895
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
doi: 10.1109/TVCG.2015.2468151
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
doi: 10.1109/TVCG.2014.2346574
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
doi: 10.1016/j.jbi.2013.12.012
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
doi: 10.1145/1961189.1961199
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
doi: 10.1109/TVCG.2014.2346913
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
doi: 10.1109/TVCG.2014.2346747
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
doi: 10.1111/j.1467-8659.2011.01918.x
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
doi: 10.1177/1473871611430769
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
doi: 10.1109/TVCG.2014.2346926
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
doi: 10.1109/TVCG.2014.2307892
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
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
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
doi: 10.1109/TVCG.2014.2346449
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
doi: 10.1109/TITS.2015.2436897
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
doi: 10.1109/TVCG.2014.2346677
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
doi: 10.1109/TVCG.2014.2346922
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
doi: 10.1109/TVCG.2014.2346919
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
doi: 10.1109/TVCG.2014.2346591
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
doi: 10.1109/TVCG.2014.2346446
32 Ma Y X, Xu J Y, Peng D C, Zhang T, Jin C Z, Qu H M, Chen W, 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
doi: 10.1007/s11390-013-1378-5
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
doi: 10.1109/TVCG.2010.82
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
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
doi: 10.1109/vast.2015.7347626
88 Chuang J, Socher R. Interactive visualizations for deep learning. In: Proceedings of the IEEE VIS Workshop on Visualization for Predictive Analytics. 2014
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
doi: 10.1145/2801040.2801054
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
doi: 10.1109/TVCG.2014.2346920
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
doi: 10.1109/vast.2010.5652443
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
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
doi: 10.1109/TVCG.2012.277
94 Munzner T. Visualization Analysis and Design. Boca Raton: CRC Press, 2014
95 Delevingne L. Hedge fund robots crushed human rivals in 2014. , 2015
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
doi: 10.1016/j.obhdp.2012.08.003
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
doi: 10.1109/TVCG.2015.2413786
98 Amini F, Rufiange S, Hossain Z, Ventura Q, Irani P, McGuffin M J. 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
doi: 10.1109/TVCG.2014.2329308
33 Van den Elzen S, Holten D, Blaas J, VanWijk J J. Dynamic network visualization with extended massive sequence views. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(8): 1087–1099
doi: 10.1109/TVCG.2013.263
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
doi: 10.1109/TVCG.2014.2346441
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
doi: 10.1109/TVCG.2015.2468078
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
doi: 10.1007/978-3-642-32498-7_5
37 Eaton C, Plaisant C, Drizd T. Visualizing missing data: graph interpretation user study. In: Proceedings of IFIP Conference on Human Computer Interaction. 2005, 861–872
doi: 10.1007/11555261_68
38 Templ M, Alfons A, Filzmoser P. Exploring incomplete data using visualization techniques. Advances in Data Analysis and Classification, 2012, 6(1): 29–47
doi: 10.1007/s11634-011-0102-y
Related articles from Frontiers Journals
[1] Chengliang WANG,Yayun PENG,Debraj DE,Wen-Zhan SONG. DPHK: real-time distributed predicted data collecting based on activity pattern knowledge mined from trajectories in smart environments[J]. Front. Comput. Sci., 2016, 10(6): 1000-1011.
[2] Xin XU,Wei WANG,Jianhong WANG. A three-way incremental-learning algorithm for radar emitter identification[J]. Front. Comput. Sci., 2016, 10(4): 673-688.
[3] Wenmei LIU,Hui LIU. Major motivations for extract method refactorings: analysis based on interviews and change histories[J]. Front. Comput. Sci., 2016, 10(4): 644-656.
[4] Mingqiang GUO,Ying HUANG,Zhong XIE. A balanced decomposition approach to real-time visualization of large vector maps in CyberGIS[J]. Front. Comput. Sci., 2015, 9(3): 442-455.
[5] Djamal ZIANI. Feature selection on probabilistic symbolic objects[J]. Front. Comput. Sci., 2014, 8(6): 933-947.
[6] Yaobin HE, Haoyu TAN, Wuman LUO, Shengzhong FENG, Jianping FAN. MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data[J]. Front. Comput. Sci., 2014, 8(1): 83-99.
[7] Heng WU, Wenbo ZHANG, Jianhua ZHANG, Jun WEI, Tao HUANG. A benefit-aware on-demand provisioning approach for multi-tier applications in cloud computing[J]. Front Comput Sci, 2013, 7(4): 459-474.
[8] Haibo MI, Huaimin WANG, Yangfan ZHOU, Michael Rung-Tsong LYU, Hua CAI, Gang YIN. An online service-oriented performance profiling tool for cloud computing systems[J]. Front Comput Sci, 2013, 7(3): 431-445.
[9] Fabian GIESEKE, Gabriel MORUZ, Jan VAHRENHOLD. Resilient k-d trees: k-means in space revisited[J]. Front Comput Sci, 2012, 6(2): 166-178.
[10] Xuesong YIN, Enliang HU. Distance metric learning guided adaptive subspace semi-supervised clustering[J]. Front Comput Sci Chin, 2011, 5(1): 100-108.
[11] Xinguang TIAN, Xueqi CHENG, Miyi DUAN, Rui LIAO, Hong CHEN, Xiaojuan CHEN, . Network intrusion detection based on system calls and data mining[J]. Front. Comput. Sci., 2010, 4(4): 522-528.
[12] Qiang YANG, . Three challenges in data mining[J]. Front. Comput. Sci., 2010, 4(3): 324-333.
[13] Lifei CHEN, Shanjun HE, Qingshan JIANG, . Validation indices for projective clustering[J]. Front. Comput. Sci., 2009, 3(4): 477-484.
[14] Bin WU , Deyong HU , Qi YE , Bai WANG , . Correlation in mobile call networks from structure perspective[J]. Front. Comput. Sci., 2009, 3(3): 347-355.
[15] Dongling ZHANG, Yong SHI, Yingjie TIAN, Meihong ZHU. A class of classification and regression methods by multiobjective programming[J]. Front Comput Sci Chin, 2009, 3(2): 192-204.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed