S. Amershi and M. Chickering, ModelTracker: Redesigning Performance Analysis Tools for Machine Learning, 2015.

G. Andrienko, N. Andrienko, P. Bak, D. Keim, and S. Wrobel, Visual Analytics of Movement, 2013.

G. Andrienko and N. Andrienko, Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions, vol.18, p.2017

G. Andrienko and N. Andrienko, Constructing Spaces and Times for Tactical Analysis in Football, TVCG, 2019.

N. Andrienko, T. Lammarsch, and G. Andrienko, Viewing Visual Analytics as Model Building, 2018.

M. Behrisch, D. Streeb, F. Stoffel, and D. Seebacher, Commercial Visual Analytics Systems-advances in the Big Data Analytics Field, vol.25, p.2019

N. Bikakis, Big Data Visualization Tools Survey, Encyclopedia of Big Data Technologies, 2019.

N. Bikakis and T. , Sellis: Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art, LWDM Workshop, 2016.

E. T. Brown and A. Ottley, Finding Waldo: Learning About Users from their Interaction TVCG, vol.20, issue.12, 2014.

D. Ceneda and T. Gschwandtner, Characterizing Guidance in Visual Analytics, TVCG, vol.23, issue.1, p.2017

J. Choo and S. Liu, Visual Analytics for Explainable Deep Learning, vol.38, 2018.

J. Chou, Y. Wang, and K. Ma, Privacy Preserving Visualization: A Study on Event Sequence Data, Comput. Graph. Forum, vol.38, issue.1, p.2019

C. Collins and N. Andrienko, Guidance in the Human Machine Analytics Process. Visual Informatics, vol.2, 2018.

C. D. Correa, Y. Chan, and K. Ma, A framework for uncertainty-aware visual analytics, 2009.

J. D. Fekete, D. Fisher, A. Nandi, and M. Sedlmair, Progressive Data Analysis and Visualization, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02090121

J. D. Fekete and R. Primet, Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01361430

D. Fisher, Trust me, I'm Partially Right: Incremental Visualization lets Analysts Explore Large Datasets Faster CHI, 2012.

T. Fujiwara, O. Kwon, and K. Ma, Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning, TVCG, vol.26, issue.1, p.2020

P. Godfrey, J. Gryz, and P. Lasek, Interactive Visualization of Large Data Sets, TKDE, vol.28, issue.8, p.2016

J. Hullman, Why Authors Don't Visualize Uncertainty, vol.26, p.2019

F. Hohman, A. Head, R. Caruana, R. Deline, and S. Drucker, Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models, 2019.

A. Kangasrääsiö, Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation, Cognitive Science, 2019.

, Mastering the Information Age: Solving Problems with Visual Analytics, Eurographics, 2010.

Y. Kim, GraphScape: A Model for Automated Reasoning about Visualization Similarity and Sequencing, 2017.

N. W. Kim, L. Shao, and M. El-assady, Quality Metrics for Information Visualization, CGF, vol.37, issue.3, 2018.

O. Kwon, T. Crnovrsanin, and K. Ma, What Would a Graph Look Like in this Layout? A Machine Learning Approach to Large Graph Visualization, TVCG, vol.24, issue.1, 2018.

O. Kwon and K. Ma, A Deep Generative Model for Graph Layout, TVCG, vol.26, issue.1, p.2020

A. Lior, J. Allen, O. Barykin, V. Borkar, and B. Chopra, SCUBA: diving into Data at Facebook, PVLDB, vol.6, issue.11, p.2013

Y. Lou, R. Caruana, J. Gehrke, and G. Hooker, Accurate Intelligible Models with Pairwise Interactions. KDD, 2013.

S. Lundberg and S. Lee, A Unified Approach to Interpreting Model Predictions, 2017.

L. Micallef, Towards Perceptual Optimization of the Visual Design of Scatterplots, TVCG, vol.23, issue.6, p.2017

L. Micallef and G. Palmas, Towards Perceptual Optimization of the Visual Design of Scatterplots, TVCG, vol.23, issue.6, p.2017

D. Moritz, Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco, TVCG, vol.25, issue.1, p.2019

B. Mutlu, E. E. Veas, and C. T. Vizrec, Recommending Personalized Visualizations, TiiS, vol.6, issue.4, p.2016

L. Po, N. Bikakis, F. Desimoni, and G. Papastefanatos, Linked Data Visualization: Techniques, Tools and Big Data, p.2020

A. Preston, M. Gomov, and K. Ma, Uncertainty-Aware Visualization for Analyzing Heterogeneous Wildfire Detections, IEEE CGA, vol.39, issue.5, p.2019

J. Poco and J. Heer, Reverse-Engineering Visualizations: Recovering Visual Encodings from Chart Images, CGF, vol.36, issue.3, p.2017

X. Qin, Y. Luo, N. Tang, and G. Li, Making Data Visualization more Efficient and Effective: A survey

M. T. Ribeiro, S. Singh, and C. Guestrin, Why Should I Trust You?": Explaining the Predictions of Any Classifier, KDD, 2016.

B. Saket, D. Moritz, H. Lin, V. Dibia, C. Demiralp et al., Beyond Heuristics: Learning Visualization Design, 2018.

M. Savva, N. Kong, A. Chhajta, L. Fei-fei, M. Agrawala et al., ReVision: Automated Classification, Analysis and Redesign of Chart Images, 2011.

B. Shneiderman, Response Time and Display Rate in Human Performance with Computers, ACM Comput. Surv, vol.16, issue.3, 1984.

N. Silva, Eye Tracking Support for Visual Analytics Systems: Foundations, Current Applications and Research Challenges, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02084137

A. Srinivasan, S. M. Drucker, A. Endert, and J. Stasko, VODER: Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication, TVCG, vol.25, issue.1, p.2019

S. Thalmann and J. Mangler, Data Analytics for Industrial Process Improvement, 2018.

C. Turkay and N. Pezzotti, Progressive Data Science: Potential and Challenges, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01961871

Y. Wang and K. Ma, Revealing the fog-of-war: A visualizationdirected, uncertainty-aware approach for exploring high-dimensional data, IEEE BigData, 2015.

X. Wang, W. Chen, J. Chou, C. Bryan, H. Guan et al., GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms, TVCG, vol.25, issue.1, p.2019

X. Wang, J. Chou, W. Chen, H. Guan, W. Chen et al., A Utility-Aware Visual Approach for Anonymizing Multi, vol.24, 2018.

M. Wattenberg and F. , Viegas: Visualization: The Secret Weapon of Machine Learning, EuroVis, vol.2017

Y. Wu, G. Yuan, and K. Ma, Visualizing Flow of Uncertainty through Analytical Processes, vol.18, p.2012

F. Zhou and X. Lin, A Survey of Visualization for Smart Manufacturing, Journal of Visualization, vol.22, issue.2, p.2019

T. Zuk and S. Carpendale, Theoretical Analysis of Uncertainty Visualizations. Visualization and Data Analysis, 2006.