Professor Min Chen - Publications

A Selection of Representative Publications

M. Chen, A. Abdul-Rahman, D. Archambault, J. Dykes, A. Slingsby, P. D. Ritsos, T. Torsney-Weir, C. Turkay, B. Bach, A. Brett, H. Fang, R. Jianu, S. Khan, R. S. Laramee, L. Matthews, P. H. Nguyen, R. Reeve, J. C. Roberts, F. Vidal, Q. Wang, J. Wood, and K. Xu. RAMPVIS: answering the challenges of building visualisation capabilities for large-scale emergency responses. Epidemics, 39:100569, 2022. DOI. (Earlier versions: https://api.newton.ac.uk/website/v0/events/preprints/NI20011, 2021; arxiv:2012.04757, 2020.)

Keywords: visualization, visual analytics, supporting epidemiological modelling, emergency responses, COVID-19, four levels of visualization.

Questions: How did the UK VIS scientists and researchers do to support epidemiologists and modelling scientists during COVID-19?

M. Chen and M. Sbert. A bounded measure for estimating the benefit of visualization (Part I): theoretical discourse and conceptual evaluation. Entropy, 24(2), 228, 2022. DOI.

M. Chen, A. Abdul-Rahman, D. Silver, and M. Sbert. A bounded measure for estimating the benefit of visualization (Part II): case studies and empirical evaluation. Entropy, 24(2), 282, 2022. DOI.

Keywords: theory of visualization, information theory, cost-benefit analysis, bounded measure, multi-criteria decision analysis, volume visualization, metro map.

Questions: How can we resolve the issue of an unbounded divergence component in the original cost-benefit formula (Chen and Golan 2016)?

M. Chen and D. S. Ebert. An ontological framework for supporting the design and evaluation of visual analytics systems. Computer Graphics Forum, 38(3):131-144, 2019. DOI. (presented at EuroVis 2019.) Supporting web site: IVAS (Improving Visual Analytics Systems).

Keywords: theory of visualization, information theory, cost-benefit analysis, bounded measure, multi-criteria decision analysis, volume visualization, metro map.

Questions: How can we systematically optimize a visual analytics workflow (or data intelligence workflow in general)? Can we use the metaphors of sympotom, cause, remedie, and side-effect to analyze the problems in a workflow and potential solutions to the problems? How do the four categories of processes (i.e., statistics, algorithms, visualization, and interaction) relate to each other when they are considered as a sympotom, cause, remedie, or side-effect?

D. Sacha, M. Kraus, D. A. Keim, and M. Chen. VIS4ML: An ontology for visual analytics assisted machine learning. IEEE Transactions on Visualization and Computer Graphics, 25(1):385-395, 2019. DOI. (Presented in IEEE VIS 2018.)

Keywords: visualization, visual analytics, machine learning, VIS4ML, ontology.

Questions: Where in a machine learning workflow can visualization be deployed to assist model-developers? Can we use an ontology to register these places? Where have been "visited" by the existing VIS4ML solutions?

N. Kijmongkolchai, A. Abdul-Rahman, and M. Chen. Empirically measuring soft knowledge in visualization. Computer Graphics Forum, 36(3):73-85, 2017. DOI. (Presented in EuroVis 2017, Slides.)

Keywords: empirical study, cost-benefit measure, knowledge, theory of visualization, information theory.

Questions: Can the role of knowledge for reducing potential distortion in visualization be confirmed using an empirical study? Does different knowledge have different impact? How do "accuracy & response time" measures relate to "benefit & cost" measures?

G. K. L. Tam, V. Kothari, and M. Chen. An analysis of machine- and human-analytics in classification. IEEE Transactions on Visualization and Computer Graphics, 23(1):71-80, 2017. DOI. (Presented in IEEE VIS 2016, VAST2016 Best Paper Award.)

Keywords: visual analytics, classification, machine learning, decision tree, model, facial expression, visualization image, information theory.

Questions: Can we explain why two decision trees developed using visualization-assisted machine learning were more accurate than those learned fully automatically? What are the model-developers' observations and speculations? What does information theory tell us?

M. Chen and A. Golan. What may visualization processes optimize? IEEE Transactions on Visualization and Computer Graphics, 22(12):2619-2632, 2016. DOI. (Presented in IEEE VIS 2016, Slides.) Earlier version: arXiv:1506.02245, Short Videos: Cost-Benefit Analysis of Data Intelligence, Can Information Theory Explain Concepts in Cognitive Science?

Keywords: theory of visualization, information theory, cost-benefit analysis, bounded measure, multi-criteria decision analysis, volume visualization, metro map.

Questions: How can we systematically optimize a visual analytics workflow (or data intelligence workflow in general)? Can we use the metaphors of symptom, cause, remedy, and side-effect to analyze the problems in a workflow and potential solutions to the problems? How do the four categories of processes (i.e., statistics, algorithms, visualization, and interaction) relate to each other when they are considered as a symptom, cause, remedy, or side-effect?

M. Chen, M. Feixas, I. Viola, A. Bardera, H.-W. Shen, and M. Sbert. Information Theory Tools for Visualization. A K Peters/CRC Press, 2016. ISBN: 9781498740937 - CAT# K26715. Also available at Amazon.

Keywords: theory of visualization, information theory, volume visualization, flow visualization, information visualization, visual analytics.

Related Tutorials: EG2016 and IEEE VIS2016 Tutorials on Information Theory in Visualization by the same author team.

M. Chen, S. Walton, K. Berger, J. Thiyagalingam, B. Duffy, H. Fang, C. Holloway, and A. E. Trefethen. Visual multiplexing. Computer Graphics Forum, 33(3):241-250, 2014. DOI. (Presented in EuroVis 2014.)

Keywords: theory of visualization, visual multiplexing, information theory, perception, cognition, multi-field visualization.

Questions: Can humans perceive multiple pieces of information related to the same location in a visualization? If so, is there any evidence in cognitive sciences and visualization applications? Can this phenomenon be explained using information theory?

R. Borgo, A. Abdul-Rahman, F. Mohamed, P. W. Grant, I. Reppa, L. Floridi, and M. Chen. An empirical study on using visual embellishments in visualization, IEEE Transactions on Visualization and Computer Graphics, 18(12):2759-2768, 2012. DOI. (Presented in IEEE VisWeek 2012.)

Keywords: visual embellishments, chart junk, visual metaphor, empirical study, dual task experiment.

Question: Do visual embellishments have positive or negative impact on memorization, visual search and concept grasping?

M. Chen, A. Trefethen, R. Banares-Alcantara, M. Jirotka, B. Coecke, T. Ertl, and A. Schmidt. From data analysis and visualization to causality discovery, IEEE Computer, 44(10):84-87, 2011. DOI.

Keywords: causality discovery technology, visual analytics, levels of causality reasoning, workflow for causality discovery.

Questions: How do statistics, algorithms, visualization, and interaction complement with each other? Can we develop a causality discovery technology, and what will be the role of visual analysis in such a technology?

M. Chen and H. Jaenicke. An Information-theoretic Framework for Visualization, IEEE Transactions on Visualization and Computer Graphics, 16(6):1206-1215, 2010. DOI. (Presented in IEEE VisWeek 2010, Slides (4.6M).)

Keywords: theory of visualization, information theory, data processing inequality, quantitative evaluation.

Question: Can information theory explain phenomena or events in visualization? Can we mathematically show that (a) the recommendation of overview first, zoom, detail on demand is a correct, (b) logarithmic scaling is informatively-optimal for some data distribution, (c) interactive visualization breaks the conditions of data processing inequality and it indicates the merits of visualization and interaction in data intelligence, and (d) double or multiple encoding can be useful?

M. Chen, D. Ebert, H. Hagen, R. S. Laramee, R. van Liere, K.-L. Ma, W. Ribarsky, G. Scheuermann, and D. Silver. Data, Information and Knowledge in Visualization, IEEE Computer Graphics and Applications, 29(1):12-19, 2009. DOI. PDF(0.2M)

Keywords: definitions, knowledge-assisted visualization, information-assisted visualization.

Questions: Is visualization a search problem? Will interaction scale with increasing data size? Should visualization evolve from interactive to knowledge-assisted visualization?

R. P. Botchen, S. Bachthaler, F. Schick, M. Chen, G. Mori, D. Weiskopf, and T. Ertl. Action-based multi-field video visualization, IEEE Transactions on Visualization and Computer Graphics, 14(4):885-899, 2008. DOI.

Keywords: video visualization, VideoPerpetuoGram (VPG).

Question: Can videos be visualized in a way similar to ECGs and seismographs?

O. Gilson, N. Silva, P.W. Grant, and M. Chen. From web data to visualization via ontology mapping, Computer Graphics Forum, 27(3):959-966, 2008. (Presented in EuroVis 2008, Best Paper Award.) DOI, PDF(1M).

Keywords: ontology, automated visualization.

Question: Is automatic generation of visualizations from domain-specific data feasible?

D. Hubball, M. Chen, and P. W. Grant. Image-based aging using evolutionary computing. Computer Graphics Forum, 27(2):607-616, 2008. DOI, PDF(0.5M). (Presented in Eurographics 2008.)

Keywords: aging simulation, facial age progression, genetic programming, data-driven.

Question: Can we simulate facial aging in a person-specific manner?

M. Chen, R.P. Botchen, R.R. Hashim, D. Weiskopf, T. Ertl, and I.M. Thornton. Visual signatures in video visualization. IEEE Transactions on Visualization and Computer Graphics, 12(5):1093-1100, 2006. DOI. (Presented in IEEE Visualization 2006.)

Keywords: video visualization, visual signature, user study.

Question: Can ordinary people learn to recognise visual signatures in video visualization?

C. Correa, D. Silver, and M. Chen. Feature aligned volume manipulation for illustration and visualization. IEEE Transactions on Visualization and Computer Graphics, 12(5):1069-1076, 2006. DOI. (Presented in IEEE Visualization 2006.)

Keywords: illustrative visualization, interactive manipulation, volume deformation, GPU

Question: How to deform volumetric models according to features?

A. Abdul-Rahman and M. Chen. Spectral volume rendering based on the Kubelka-Munk theory. Computer Graphics Forum, 24(3), 2005. DOI. (Presented in Eurographics 2005.)

Keywords: volume rendering integral, Kubelka-Munk theory, spectral rendering.

Question: Does Kubelka-Munk theory offer a better volume rendering integral?

G. W. Daniel and M. Chen. Video visualization. Proc. IEEE Visualization 2003, 409-416, Seattle, WA, October 2003. PDF(10M).

Keywords: video visualization, metrics for change detection and image comparison, horse-shoe visual design.

Question: Can we use a single image to visualize a video?

S. Treavett and M. Chen. Pen-and-Ink rendering in volume visualization. Proc. IEEE Visualization 2000, Salt Lake City, Utah, Ertl, Hamann and Varshney (eds.), 203-209, IEEE Computer Society, October, 2000. PDF(450K).

Keywords: non-photorealistic rendering (NPR), illustrative visualization, volume visualization.

Question: How can we create an illustrative volume visualization using non-photo-realistic rendering (NPR)?

M. Chen and J. V. Tucker. Constructive volume geometry. Computer Graphics Forum, 19(4):281-293, 2000. DOI, PDF(450K). (Presented in the International Workshop on Volume Graphics at Swansea in March 1999.)

Keywords: theoretic framework, volume modelling, volume rendering, multi-field, volume scene graph, constructive volume geometry (CVG).

Question: How can we model and render multiple volumes (scalar fields) in a constructive manner (in a way similar to constructive solid geometry (CSG)? How can we sample continuous fields (e.g., implicit surfaces) and discrete fields (e.g., volume datasets) consistently?