CS 91.540 — Visual Analytics - Spring 2010

Home , George Furnas, IEEE Visualization, Pat Hanrahan, Sheelagh Carpendale

Description Whether the cause is an epidemic, natural disaster or an act of terrorism, accurate prediction of risks, timing, and consequences of destructive events can prevent loss of life and damage to property and natural resources. Whether one is looking for a genetic marker, a trend in the stock market, patterns in educational settings, quality of life indicators, accurate analysis can help the discovery process, provide insights, and identify and measure ambiguities and uncertainties.

Visual analytics is the use of information and communication technologies designed to aid analysts in a variety of fields from health, manufacturing, security, education, finance, scientific and social science research, and others, deal with massive, dynamic sets of structured (spreadsheets, databases) and unstructured data (text, web pages, video), and often conflicting data. Analysts use these technologies to provide timely, defensible, and understandable assessments and to communicate these effectively for action to both the public and decision-makers. The overall goal is to detect the expected and discover the unexpected.

What you will get out of the course A student who successfully fulfills the course requirements will have demonstrated an understanding of the fundamentals of visual analytics and its applications, an understanding of the analytical reasoning process, an understanding of cognition, perception, and designing for human users, an ability to design, build, and evaluate suitable visual representations to a real-world dataset for decision makers or the public.

The course The course will discuss and combine principles from cognitive science, information visualization, geospatial information systems, machine-based reasoning and learning, and data mining. There will be a number of guest lecturers. Several focus areas include

Analytical reasoning techniques that enable users to obtain deep insights that directly support assessment, planning, and decision making; Visual representations and interaction techniques that take advantage of human vision’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once; Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis; Techniques that support the production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences.

This course is about the what and why of visual analytics, and in some sense the ‘how an analyst does visual analytics’. Our goal is to build an understanding of basic ideas in visual analytics, then focus on understanding the analysis process--what is it we do when we use the tools? Students will be exposed to both basic visual analytics tools and as well to some of the latest research and commercial-grade visual analytic tools. Analytic reasoning is a process that encompasses perception, cognition, discourse, and collaboration. This course considers methods and tools that support analytic reasoning by combining human visual capabilities with computational devices and algorithms. Topics include data representation and transformation, visual representation and interaction, production, presentation, and dissemination of knowledge, sense-making, and the challenges that information complexity and scalability pose for the very human process of reasoning.

Goals of course The goals of this course are for students to develop a comprehensive understanding of this emerging, multidisciplinary field; apply that understanding to a tightly focused research/discovery problem in a domain of personal interest

Class format The class format will include reading, discussion, and application of existing software environments to problems in visual analytics. Class meetings will typically include: (1) discussion of two or three journal or conference publications that cover a range of topics from information visualization, geographic information systems, visual data mining, cognitive science, user- centered design, and the semantic web; (2) learning about, applying, extending, or assessing aspects of visual analysis tools and techniques. Students will be expected to take an active role in discussions, with selected students assigned the role of organizing and leading most discussions.

Class project The class project is the main examination criteria for this course. Projects may be carried out individually or in groups of two, and will be research projects within the field of visual analytics. Students will choose a topic by the second week of classes, and will work on their project for the duration of the semester. Topics can come from such areas as finance (stock market, credit), health care (homeless, emergency room patterns), geospatial (toxic use, disease spread, world indicators), medicine (genomics, drug discovery), education (measures, patterns), law enforcement (money laundering, capital crimes), homeland security (avert terrorism, drug traffic, secure borders), national security (intelligence, information access), and information technology (cyber terrorism, internet security, network analysis).

Instructor approval is necessary for all topics, and the instructors will also furnish students with some suggestions for suitable topics during the first week of classes. The project will focus on building, applying, or evaluating visual analytics methods and tools. Teams will present their results and submit a paper with content, style, length, and quality typical of current conference proceedings.

Projects that are particularly promising may be selected by the instructors to be further developed into research papers suitable for submission to the IEEE Symposium on Visual Analytics Science and Technology (VAST 2010), with a submission deadline on March 31st, 2010.

More details on the class project will be given in a separate handout during the first week of classes.

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Assignments

Assignment 7

Using your chosen project dataset, use a combination of visualization and analysis to write up problems, discoveries, and insights.

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Assignment 6

Read the sixth chapter of Illuminating the Path. Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract). You may skip this part of the assignment if you are presenting a paper this week. Using your chosen dataset, provide some hypotheses you might aim for. Discuss your data set and write up the kinds of problems, discoveries, and insights that you are looking for. Visualize the dataset using a visualization system of your choice.

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Assignment 5

Read the fifth chapter of Illuminating the Path. Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract). Sign up to give a presentation on a visual analytics topic. Refer to the reading list for details.

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Assignment 4

Read the fourth chapter of Illuminating the Path Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract) Run Tableau on a dataset of your choice, write up issues and key discoveries Post a link to the source of your chosen dataset with a brief description on your group page.

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Assignment 3

Read the third chapter of Illuminating the Path Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract) Install and run Tableau on a dataset of your choice Start looking for a dataset to work with on your project

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Assignment 2

Read the second chapter of Illuminating the Path Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract) Use R or an R derivative to analyze the new assigned data set - write up issues and key discoveries. Select area of interest for class discussion.

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Assignment 1

Read the first chapter of Illuminating the Path Read one paper you’re interested in from the references and summarize that paper (<1 page – do NOT copy the abstract) Take the Challenger Data set and produce a report that convinces me NOT to launch You will get an email about how to place your results on the class web page

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DataSets

Visual Analysis Examples from the VAST Contest

From the Visual Analytics Benchmark Repository http://hcil.cs.umd.edu/localphp/hcil/vast/archive/

Award: Excellent analytical technique featuring integration of data mining and visual analytics

http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/UKN- AVA1/traffic/traffic_11/UKN-KNIME-MC1/UKN-KNIME-MC1/index.htm

Award: Excellent example of Analytic tradecraft

http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/VIS(US)%20Stuttgart/gr and/grand_19/visus-stuttgart-gc/index.htm

Award: Good Analytical debrief

http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/sztaki/flitter/flitter _3/sztaki-socialVis-MC2/sztaki-socialVis-MC2/index.htm

Award: Intuitive analytic information presentation:

http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/gami/traffic/traffic_1 /gami-BNT-MC1/gami-BNT-MC1/index.htm

Award: Intuitive traffic visualization and video description of the analysis process

http://vastsubmission.cs.uml.edu/ChallengeSubmissions/2009/Palantir_VAST09/traffi c/traffic_15/Palantir_MC1/Palantir_MC1/index.htm

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Visual Analysis Resources from Tableau

Tableau Videos

Zen of Visual Analysis Exploring New and Unfamiliar Data

http://www.tableausoftware.com/learning/training/catalog

Tableau White Papers on the Visual Analysis Process

Visual Analysis for Everyone: Understanding Data Exploration and Visualization

http://www.tableausoftware.com/whitepapers/visual-analysis-everyone

Tableau Visualization Snapshots with Captions and Descriptions

Includes links to the corresponding Tableau workbook

http://www.tableausoftware.com/learning/examples

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The following are data sets for various homeworks and for projects:

Challenger Data

The Challenger Data is available in three formats, the HTML file which describes and contains the data, an Excel spreadsheet, and a text file which contains the data in CSV format.

Challenger_data.html

Challenger_data.xls

Challenger_data.txt

Breast Cancer Data

Breast_Cancer.csv

Text Mining Data

Small_Document_Collection.zip

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Reading List

CONTINUALLY BEING UPDATED

This is the full list of all relevant readings. If the reading's link goes to the content itself (e.g., it's not just a link to the work's page on Amazon), the entry is marked ONLINE.

PLEASE NOTE: Some of the readings are copyrighted and are stored in a password- protected directory. To access these readings, provide as both username and password the 5-digit UML course number of the Visual Analytics course.

Textbooks (ONLINE)

1. Illuminating the Path: The R&D Agenda for Visual Analytics James J. Thomas and Kristin A. Cook. Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society, 2005. ISBN: 0-7695-2323-4

The first reference to the role of visual analytics in homeland security. Topical in nature and valuable for highlighting one aspect of visual analytics. Perhaps its one weakness is that it has led to the interpretation that visual analytics is solely the domain of homeland security.

2. Psychology of Intelligence Analysis Richard J. Heuer. Psychology of Intelligence Analysis. Central Intelligence Agency: Center for the Study of Intelligence, 1999.

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Readings - Papers are organized per topic covered in class

Some of the papers are ONLINE. You can select others not in this list. Please send me a copy and I'll add it to the right section.

Week 1-5 – I will discuss what visual analytics is and ought to be or will become. I will lead the discussions of the chapters and papers.

Weeks 6 and 15 – You will lead the discussions of various topics related to the papers you read. The discussion will be lead by a student (with my help) but everyone in class will participate. All the readers of papers on a particular topic will participate. Others who have read other papers can also participate and bring forth ideas. The discussions should focus on the paper and its role with visual analytics. What is the one takeaway point of the paper? What insight does it provide about visual analytics and its process? What are key statements it makes and key research topics it implies? Are any papers in the references worthwhile reads?

You may find additional papers not in the following list that cover the topics below (or others as discussed in class). Select what you feel are the most important papers and discuss them with respect to the analysis process.'''

If the paper is not on the class papers page send me its name and I’ll add it (this does not have to be a paper you select for review). If the paper is already selected by someone you must find another. Send me the name of the papers you selected and I’ll place a link to your web page for their review. For each of your papers write up a one paragraph summary and a one page review. Place both on your web page after the paper title (please do not copy the abstract, just paraphrase or explain, …). Generate PowerPoint slides related to the paper and place them on your page as well. Your page should then have paper title followed by a summary and two links to the review and the PowerPoint slides.

Introduction and Overview

D. A. Keim, F. Mansmann, J. Schneidewind, and H. Ziegler. Challenges in visual data analysis. In Proceedings of the Tenth International Conference on Information Visualization, pages 9–16, 2006. D. Keim, G. Andrienko, J-D. Fekete, C. Görg, J. Kohlhammer and G. Melançon. Chapter 7. Visual Analytics: Definition, Process, and Challenges, pages 154–175, volume 4950 of LNCS State-of-the-Art Survey, Springer, 2008. [http://geoanalytics.net/and/papers/springer08b.pdf] John Stasko, Carsten Goerg, and Zhicheng Liu, "Jigsaw: Supporting Investigative Analysis through Interactive Visualization", Information Visualization, Vol. 7, No. 2, Summer 2008, pp. 118-132. [http://www.cc.gatech.edu/~john.stasko/papers/iv08-jigsaw.pdf] Remco Chang, Mohammad Ghoniem, Robert Korsara, William Ribarsky, Jing Yang, Evan Suma, Carolina Ziemkiewicz, Daniel Kern, Agus Sudjianto. WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions. IEEE Visual Analytics Science and Technology (VAST) 2007. [http://www.viscenter.uncc.edu/TechnicalReports/CVC-UNCC-07-13.pdf] Pak Chung Wong, Kevin Schneider, Patrick Mackey, Harlan Foote, George Chin Jr., Ross Guttromson, Jim Thomas ‚A Novel Visualization Technique for Electric Power Grid Analytics,‛ IEEE Transactions on Visualization and Computer Graphics 15(3):410-423.

The Analysis Process

Daniel M. Russell, Mark J. Stefik, Peter Pirolli, and Stuart K. Card. The cost structure of sensemaking. In Proceedings of the ACM INTERCHI '93 Conference on Human Factors in Computing Systems, pp. 269–276, 1993. Peter Pirolli and Stuart Card. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proceedings of International Conference on Intelligence Analysis, 2005. Hetzler, E.; Turner, A., Analysis experiences using information visualization, CG&A, Vol. 24, No. 5, 2004. D. A. Keim, F. Mansmann, J. Schneidewind, and H. Ziegler. Challenges in visual data analysis. In Proceedings of the Tenth International Conference on Information Visualization, pages 9–16, 2006. B. Shneiderman. The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of the IEEE Symposium on Visual Languages, pages 336–343. IEEE Computer Society Press, 1996. Zhicheng Liu, Nancy J. Nersessian, John T. Stasko. Distributed Cognition as a Theoretical Framework for Information Visualization. Transactions on IEEE Visualization and Computer Graphics, Vol. 14, No. 6. (November 2008), pp. 1173-1180. R. A. Amar, J. Eagan, and J. T. Stasko. Low-level components of analytic activity in information visualization. In Proceedings of the IEEE Symposium on Information Visualization, pages 111–117, 2005. William Wright, David Schroh, Pascale Proulx, Alexander W. Skaburskis, Brian Cort: The sandbox for analysis: concepts and Evaluation. CHI 2006: 801-810. Yedendra Shrinivasan and Jarke van Wijk. Supporting the analytical reasoning process in information visualization. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 1237–1246, 2008. Jeffrey Heer, Jock D. Mackinlay, Chris Stolte, Maneesh Agrawala. Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation. IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis'08), 14(6):1189–1196, Nov/Dec 2008.

Cognition and Perception

H. Hagh-Shenas, S. Kim, V. Interrante, and C. G. Healey. Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. IEEE Transactions on Visualization and Computer Graphics, 13(6):1270–1277, 2007. C. G. Healey. Choosing effective colours for data visualization. In Proceedings of the IEEE Conference on Visualization, pages 263–270, 1996. H. Levkowitz and G. T. Herman. Color scales for image data. IEEE Computer Graphics and Applications, 12(1):72–80, Jan. 1992 Jörn Schneidewind, Daniel Keim, Mike Sips. Pixnostics: Towards Measuring the Value of Visualization. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, 2006. L. D. Bergman, B. E. Rogowitz, L. A. Treinish, A rule-based tool for assisting colormap selection, Procceedings of IEEE Visualization 1995, 1995. J.-D. Fekete, J.J. van Wijk, J.T. Stasko, C. North, The Value of Information Visualization. In: A. Kerren, J.T. Stasko, J.-D. Fekete, C. North (eds.), Information Visualization - Human-Centered Issues and Perspectives. LNCS 4950, Springer, p. 1-18, 2008. [http://www.win.tue.nl/~vanwijk/infovis_springer.pdf] Robert Kosara, Silvia Miksch, Helwig Hauser. Semantic Depth of Field. Proceedings of IEEE InfoVis, pp. 97–104, 2001.

Data Representations

N. Elmqvist, J.-D. Fekete. Hierarchical Aggregation for Information Visualization: Overview, Techniques and Design Guidelines. In IEEE Transactions on Visualization and Computer Graphics, to appear, 2009. [https://engineering.purdue.edu/~elm/projects/hieragg/hieragg.pdf] M. C. F. de Oliveira and H. Levkowitz. From visual data exploration to visual data mining: A survey. IEEE Transactions on Visualization and Computer Graphics, 9(3):378–394, July/Sept. 2003 A. Dix and G. Ellis. By chance - enhancing interaction with large data sets through statistical sampling. In Proceedings of the ACM Conference on Advanced Visual Interfaces, pages 167–176, 2002. F. Olken and D. Rotem. Simple random sampling from relational databases. In Proceedings of the Conference on Very Large Data Bases, pages 160–169, Los Altos, CA 94022, USA, 1986. A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Computing Surveys, 31(3):264–323, 1999. D. Holten. Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on Visualization and Computer Graphics, 12(5):741–748, 2006. N. Elmqvist, T.-N. Do, H. Goodell, N. Henry, and J.-D. Fekete. ZAME: Interactive large-scale graph visualization. In Proceedings of the IEEE Pacific Visualization Symposium, pages 215–222, 2008. J. Abello, F. van Ham, and N. Krishnan. ASK-GraphView: A large scale graph visualization system. IEEE Transactions on Visualization and Computer Graphics, 12(5):669–676, 2006. J. Abello and F. van Ham. Matrix Zoom: A visual interface to semiexternal graphs. In Proceedings of the IEEE Symposium on Information Visualization, pages 183–190, 2004. P. Eades and Q.-W. Feng. Multilevel visualization of clustered graphs. In Proceedings of the Symposium on Graph Drawing, number 1190 in Lecture Notes in Computer Science, pages 101–112, 1996.

Visual Representations

R. A. Amar, J. Eagan, and J. T. Stasko. Low-level components of analytic activity in information visualization. In Proceedings of the IEEE Symposium on Information Visualization, pages 111–117, 2005. L. Byron and M. Wattenberg. Stacked graphs - geometry & aesthetics. IEEE Transactions on Visualization and Computer Graphics, 14(6):1245–1252, 2008. J. Heer and G. Robertson. Animated transitions in statistical data graphics. IEEE Transactions on Visusalization and Computer Graphics, 13(6):1240–1247, 2007. F. Bendix, R. Kosara, and H. Hauser. Parallel sets: A visual analysis of categorical data. In Proceedings of the IEEE Symposium on Information Visualization, pages 133–140, 2005. Remco Chang, Mohammad Ghoniem, Robert Kosara, William Ribarsky, Jing Yang, Evan Suma, Caroline Ziemkiewicz, Daniel Kern, Agus Sudjianto. WireVis : Visualization of Categorical, Time-Varying Data From Financial Transactions. In Proceedings of the 2nd IEEE Symposium on Visual Analytics Science and Technology (VAST’07), 2007. N. Elmqvist, P. Dragicevic, J.-D. Fekete. Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation. In IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2008), 14(6):1141-1148, 2008. A. Inselberg. Multidimensional detective. In IEEE Symposium on Information Visualization, pages 100–107, 1997. C. Stolte, D. Tang, and P. Hanrahan. Multiscale visualization using data cubes. IEEE Transactions on Visualization and Computer Graphics, 2003. C. Stolte, D. Tang, and P. Hanrahan. Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Transactions on Visualization and Computer Graphics, 8(1):52–65, 2002. T. Munzner, F. Guimbretiere, S. Tasiran, L. Zhang, and Y. Zhou. TreeJuxtaposer: scalable tree comparison using focus+context with guaranteed visibility. In Proceedings of ACM SIGGRAPH 2003, pages 453–462, 2003.

Interaction

J. S. Yi, Y. ah Kang, J. T. Stasko, and J. A. Jacko. Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions of Visualization and Computer Graphics, 13(6):1224–1231, 2007. G.W. Furnas. A fisheye follow-up: further reflections on focus + context. In Proceedings of the ACM CHI 2006 Conference on Human Factors in Computing Systems, pages 999–1008, 2006. G. W. Furnas and B. B. Bederson. Space-scale diagrams: Understanding multiscale interfaces. In Proceedings of the ACM CHI’95 Conference on Human Factors in Computing Systems, pages 234–241, 1995. J. J. van Wijk and W. A. A. Nuij. Smooth and efficient zooming and panning. In Proceedings of the IEEE Symposium on Information Visualization, pages 15–22, 2003. Matthew O. Ward and Jing Yang. Interaction Spaces in Data and Information Visual- ization. In Proceedings of the Joint Eurographics - IEEE TCVG Symposium on Visualization, pp. 137–146, 2004. Wesley Willett, Jeffrey Heer, Maneesh Agrawala. Scented Widgets: Improving Navigation Cues with Embedded Visualizations. Proceedings of the IEEE Conference on Information Visualization (InfoVis) 2007. [PDF]

Communication

Jeffrey Heer, Fernanda B. Viégas, Martin Wattenberg. Voyagers and Voyeurs: Supporting asynchronous collaborative information visualization. In Proceedings of the ACM CHI Conference on Human Factors in Computing Systems, pp. 1029–1036, 2007. Dennis P. Groth and Kristy Streefkerk. Provenance and Annotation for Visual Exploration Systems. IEEE Transactions on Visualization and Computer Graphics, 12(6):1500–1510, 2006. Fernanda B. Viégas and Martin Wattenberg. Communication-minded visualization: A call to action. IBM Systems Journal, 45(4):801–812, 2006. Ryan Eccles, Thomas Kapler, Robert Harper, and William Wright. Stories in GeoTime. In Proceedings of the IEEE Symposium on Visual Analytics Science & Technology, pp. 19–26, 2007. Zachary Pousman, John T. Stasko, and Michael Mateas. Casual Information visualization: Depictions of data in everyday life. IEEE Transactions on Visualization and Computer Graphics, 13(6):1145–1152, 2007.

Collaborative Visual Analytics

S. E. Brennan, K. Mueller, G. Zelinsky, I. Ramakrishnan, D. S. Warren, and A. Kaufman. Toward a multi-analyst, collaborative framework for visual analytics. In Proceedings of the IEEE Symposium on Visual Analytics Science & Technology, pages 129–136, 2006. Fernanda B. Viégas, Martin Wattenberg, Frank van Ham, Jesse Kriss, Matt Mckeon. ManyEyes: a site for visualization at internet scale. IEEE Transactions on Visualization and Computer Graphics, 13(6):1121-1128, 2007. Petra Isenberg and Danyel Fisher. Collaborative Brushing and Linking for Co- located Visual Analytics of Document Collections. Computer Graphics Forum (Proceedings of EuroVis), 28(3):1031–1038, June 2009. [PDF] Petra Isenberg and Sheelagh Carpendale. Interactive Tree Comparison for Co- located Collaborative Information Visualization. IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2007), 13(6):1232–1239, November/December 2007. [PDF]. Jeffrey Heer, Maneesh Agrawala. Design considerations for collaborative visual analytics. Information Visualization, 7(1):49-62, March 2008. [PDF]

Evaluation

Chris North. Towards Measuring Visualization Insight. IEEE Computer Graphics & Applications, 26(3):6–9, 2006. Sheelagh Carpendale. Evaluating Information Visualizations Information Visualization. In Information Visualization: Human-Centered Issues and Perspectives, LNCS 4950: 19–45, 2008. Catherine Plaisant, Jean-Daniel Fekete, and Georges Grinstein. Promoting insight-based evaluation of visualizations: From contest to benchmark repository. IEEE Transactions on Visualization and Computer Graphics, 14(1):120– 134, 2008. Catherine Plaisant. The Challenge of Information Visualization Evaluation. In Proceedings of the ACM Conference on Advanced Visual Interfaces, pp. 109–116, 2004. Jean Scholtz. Beyond Usability: Evaluation Aspects of Visual Analytic Environments. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, 2006. Ben Shneiderman and Catherine Plaisant. Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In Proceedings of BEyond time and errors: novel evaLuation methods for InfoVis, pp. 1–7, 2006.

Advanced Topics

Yuri A. Ivanov, Christopher R. Wren, Alexander Sorkin, Ishwinder Kaur. Visualizing the History of Living Spaces. IEEE TVCG 13(6): 1153-1160, Nov-Dec 2007. SungYe Kim, Ross Maciejewski, Karl Ostmo, Edward J Delp, Timothy F Collins, David S Ebert, Mobile analytics for emergency response and training, Information Visualization (2008) 7, pp.77-88, 2008. [PDF] Kim, S., Jang, Y., Mellema, A., Ebert, D., Collins, T., "Visual Analytics on Mobile Devices for Emergency Response,"IEEE Symposium on Visual Analytics Science and Technology (VAST), pp.35-42, 2007. [PDF]. Pattath, A., Bue, B., Jang, Y., Ebert, D., Zhong, X., Ault, A., Coyle, E., "Interactive Visualization and Analysis of Network and Sensor Data on a Mobile Device", IEEE Symposium on Visual Analytics Science and Technology 2006, 2006. [PDF]. Jock D. Mackinlay, Pat Hanrahan, and Chris Stolte. Show Me: Automatic Presentation for Visual Analysis. IEEE Transactions on Visualization and Computer Graphics, 13(6):1137–1144, 2007. Bongshin Lee, Greg Smith, George G Robertson, Mary Czerwinski, Desney S Tan. FacetLens: Exposing Trends and Relationships to Support Sensemaking within Faceted Datasets. In Proceedings of the ACM CHI 2009 Conference on Human Factors in Computing Systems, pp. 1293–1302, 2009.