Treemaps for Space-Constrained Visualization of Hierarchies
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Stable Treemaps Via Local Moves
Stable treemaps via local moves Citation for published version (APA): Sondag, M., Speckmann, B., & Verbeek, K. A. B. (2018). Stable treemaps via local moves. IEEE Transactions on Visualization and Computer Graphics, 24(1), 729-738. [8019841]. https://doi.org/10.1109/TVCG.2017.2745140 DOI: 10.1109/TVCG.2017.2745140 Document status and date: Published: 01/01/2018 Document Version: Accepted manuscript including changes made at the peer-review stage Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. -
The Fourth Paradigm
ABOUT THE FOURTH PARADIGM This book presents the first broad look at the rapidly emerging field of data- THE FOUR intensive science, with the goal of influencing the worldwide scientific and com- puting research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud- computing technologies. This collection of essays expands on the vision of pio- T neering computer scientist Jim Gray for a new, fourth paradigm of discovery based H PARADIGM on data-intensive science and offers insights into how it can be fully realized. “The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science.” —Bill GaTES “I often tell people working in eScience that they aren’t in this field because they are visionaries or super-intelligent—it’s because they care about science The and they are alive now. It is about technology changing the world, and science taking advantage of it, to do more and do better.” —RhyS FRANCIS, AUSTRALIAN eRESEARCH INFRASTRUCTURE COUNCIL F OURTH “One of the greatest challenges for 21st-century science is how we respond to this new era of data-intensive -
The Diversity of Data and Tasks in Event Analytics
The Diversity of Data and Tasks in Event Analytics Catherine Plaisant, Ben Shneiderman Abstract— The growing interest in event analytics has resulted in an array of tools and applications using visual analytics techniques. As we start to compare and contrast approaches, tools and applications it will be essential to develop a common language to describe the data characteristics and diverse tasks. We propose a characterisation of event data along 3 dimensions (temporal characteristics, attributes and scale) and propose 8 high-level user tasks. We look forward to refining the lists based on the feedback of workshop attendees. KEYWORDS having the same time stamp, medical data are often Temporal analysis, pattern analysis, task analysis, taxonomy, big recorded in batches after the fact. data, temporal visualization o The relevant time scale may vary (from milliseconds to years), and may be homogeneous or not. INTRODUCTION o Data may represent changes over time of a status The growing interest in event analytics (e.g. Aigner et al, 2011; indicator, e.g. changes of cancer stages, student status or Shneiderman and Plaisant, 2016) has resulted in an array of novel physical presence in various hospital services, or may tools and applications using visual analytics techniques. As represent a set of events or actions that are not exclusive researchers compare and contrast approaches it will be helpful to from one another, e.g. actions in a computer log or series develop a common language to describe the diverse tasks and data of symptoms and medical tests. characteristics analysts encounter. o Patterns may be very cyclical or not, and this may vary The methodology of design studies - which primarily focus on over time. -
Treemap User Guide
TreeMap User Guide Macrofocus GmbH Version 2019.8.0 Table of Contents Introduction. 1 Getting started . 2 Load and filter the data . 2 Set-up the visualization . 5 View and analyze the data. 7 Fine-tune the visualization . 12 Export the result. 15 Treemapping . 16 User interface . 20 Menu and toolbars. 20 Status bar . 24 Loading data . 25 File-based data sources. 25 Directory-based data sources . 31 Database connectivity. 32 On-line data sources . 33 Automatic default configuration . 33 Data types . 33 Configuration panel . 36 Layout . 38 Group by. 53 Size. 56 Color . 56 Height . 61 Labels . 61 Tooltip. 63 Rendering . 66 Legend . 67 TreeMap view . 69 Zooming . 69 Drilling . 70 Probing and selection . 70 TreePlot view. 71 Configuration . 71 Zooming . 72 Drilling . 72 Probing and selection . 72 TreeTable view . 73 Sorting . 73 Probing and selection . 74 Filter on a subset . 75 Search . 76 Filter . 76 See details. 77 Configure variables . 78 Formatting patterns. 78 Expression. -
Treemap Art Project
EVERY ALGORITHM HAS ART IN IT Treemap Art Project By Ben Shneiderman Visit Exhibitions @ www.cpnas.org 2 tree-structured data as a set of nested rectangles) which has had a rippling impact on systems of data visualization since they were rst conceived in the 1990s. True innovation, by denition, never rests on accepted practices but continues to investigate by nding new In his book, “Visual Complexity: Mapping Patterns of perspectives. In this spirit, Shneiderman has created a series Information”, Manuel Lima coins the term networkism which of prints that turn our perception of treemaps on its head – an he denes as “a small but growing artistic trend, characterized eort that resonates with Lima’s idea of networkism. In the by the portrayal of gurative graph structures- illustrations of exhibition, Every AlgoRim has ART in it: Treemap Art network topologies revealing convoluted patterns of nodes and Project, Shneiderman strips his treemaps of the text labels to links.” Explaining networkism further, Lima reminds us that allow the viewer to consider their aesthetic properties thus the domains of art and science are highly intertwined and that laying bare the fundamental property that makes data complexity science is a new source of inspiration for artists and visualization eective. at is to say that the human mind designers as well as scientists and engineers. He states that processes information dierently when it is organized visually. this movement is equally motivated by the unveiling of new In so doing Shneiderman seems to daringly cross disciplinary is exhibit is a project of the knowledge domains as it is by the desire for the representation boundaries to wear the hat of the artist – something that has Cultural Programs of the National Academy of Sciences of complex systems. -
Quantitative Literacy to New Quantitative Literacies
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/319160466 Quantitative Literacy to New Quantitative Literacies Chapter · May 2017 CITATIONS READS 0 39 3 authors, including: Rohit Mehta James P. Howard California State University, Fresno Johns Hopkins University 32 PUBLICATIONS 42 CITATIONS 13 PUBLICATIONS 0 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: MSU-Wipro STEM & Leadership View project I Wonder: Research To Practice View project All content following this page was uploaded by Rohit Mehta on 18 October 2017. The user has requested enhancement of the downloaded file. 5 Quantitative Literacy to New Quantitative Literacies Jeffrey Craig, Rohit Mehta; James P. Howard II Michigan State University; University of Maryland University College 5.1 Introduction Steen and colleagues [42] made their Case for Quantitative Literacy based on the premise that the 21st-century, pri- marily due to technology changes, is a significantly more quantitative environment than any previous time in history. The design team who wrote the case made rhetorical observations about the increasing prevalence of numbers in society in the United States; phrases like [a] world awash in numbers [42] precede nearly every piece of literature regarding quantitative literacy (or numeracy). The authors used this rhetoric to position people relative to the demands of social systems in the world. Specifically, numeracy has emerged as a partner to literacy because the social world is being integrated with numbers due to recent technological advances. Steen argued that as the printing press gave the power of letters to the masses, so the computer gives the power of number to ordinary citizens [40, p. -
Surfacing Visualization Mirages
Surfacing Visualization Mirages Andrew McNutt Gordon Kindlmann Michael Correll University of Chicago University of Chicago Tableau Research Chicago, IL Chicago, IL Seattle, WA [email protected] [email protected] [email protected] ABSTRACT In this paper, we present a conceptual model of these visual- Dirty data and deceptive design practices can undermine, in- ization mirages and show how users’ choices can cause errors vert, or invalidate the purported messages of charts and graphs. in all stages of the visual analytics (VA) process that can lead These failures can arise silently: a conclusion derived from to untrue or unwarranted conclusions from data. Using our a particular visualization may look plausible unless the an- model we observe a gap in automatic techniques for validating alyst looks closer and discovers an issue with the backing visualizations, specifically in the relationship between data data, visual specification, or their own assumptions. We term and chart specification. We address this gap by developing a such silent but significant failures visualization mirages. We theory of metamorphic testing for visualization which synthe- describe a conceptual model of mirages and show how they sizes prior work on metamorphic testing [92] and algebraic can be generated at every stage of the visual analytics process. visualization errors [54]. Through this combination we seek to We adapt a methodology from software testing, metamorphic alert viewers to situations where minor changes to the visual- testing, as a way of automatically surfacing potential mirages ization design or backing data have large (but illusory) effects at the visual encoding stage of analysis through modifications on the resulting visualization, or where potentially important to the underlying data and chart specification. -
Ways of Visualizing Data on Curves
Edinburgh Research Explorer Ways of Visualizing Curves Citation for published version: Bach, B, Ren, Q, Perin, C & Dragicevic, P 2018, Ways of Visualizing Curves. in Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018. Edinburgh, UK, The Latent Image, Edinburgh, United Kingdom, 18/04/18. https://doi.org/10.6084/m9.figshare.6104705 Digital Object Identifier (DOI): 10.6084/m9.figshare.6104705 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018 General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 24. Sep. 2021 Transimage 2018 Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018 Ways of Visualizing Data on Curves Benjamin Bach [email protected] Charles Perin [email protected] University of Edinburgh, Edinburgh, UK City University, London, UK Qiuyuan Ren [email protected] Pierre Dragicevic [email protected] University of Edinburgh, Edinburgh, UK Inria, Saclay, France Visual variables used to encode data on the curve in the enrichment stage. -
Immersive Data Visualization and Storytelling Based on 3D | Virtual Reality Platform: a Study of Feasibility, Efficiency, and Usability
Masterarbeit Truong Vinh Phan Immersive Data Visualization and Storytelling based on 3D | Virtual Reality Platform: a Study of Feasibility, Efficiency, and Usability Fakultät Technik und Informatik Faculty of Engineering and Computer Science Department Informatik Department of Computer Science Truong Vinh Phan Immersive Data Visualization and Storytelling based on 3D | Virtual Reality Platform: a Study of Feasibility, Efficiency, and Usability Masterarbeit eingereicht im Rahmen der Masterprüfung im Studiengang Angewandte Informatik am Department Informatik der Fakultät Technik und Informatik der Hochschule für Angewandte Wissenschaften Hamburg Betreuender Prüfer: Prof. Dr. Kai von Luck Zweitgutachter: Prof. Dr. Philipp Jenke Abgegeben am October 7, 2016 Truong Vinh Phan Thema der Masterarbeit Immersive Datenvisualisierung und Storytelling, die auf 3D bzw. virtueller Realität-Plattform basiert: eine Studie der Machbarkeit, Effizienz und Usability. Stichworte immersive Datenvisualisierung, 3D, visueller Data-Mining, virtuelle Realität, Open-Data, Big- Data, UX, Userbefragung Kurzzusammenfassung Seit der Datenexplosion dank der Open-Data- bzw. Transparenz-Bewegung sind Daten- analyse und -exploration eine zwar interessanter aber immer schwieriger Herausforderung, nicht nur für die Informationstechnik und Informatik sondern auch für unsere allgemeine Gesellschaft, geworden. Wegen der Arbeitsweise des menschlichen Gehirns ist Visual- isierung eine der ersten Go-to Methoden, um komplexe Datensätze verständlich, anschaulich und zugänglich zu machen. -
Juan Morales Data Scientist
Juan Morales Data Scientist Researcher in Visual Analytics and Human-Computer Interaction. Programming lover. Avid learner. Personal Information Name: Juan Morales del Olmo Birth Date: July 18th, 1985 Homepage: http://bit.ly/juanmorales Education 2009–2013 PhD on Advanced Computing for Science and Engineering, Universidad Politécnica de Madrid (UPM), Madrid, Graduated with honor, Cum laude. 2008–2009 Artificial Intelligence MSc, Universidad Politécnica de Madrid (UPM), Madrid, pending Final Project. 2003–2008 Computer Science, Universidad Politécnica de Madrid (UPM), Madrid, Grade. 90th percentile by marks Research Experience 2013–Present Postdoc Researcher, UPM, Madrid. Developing interactive tools that help neuroscientist make sense of their data. Designing the architecture of the Visualization Framework to be used in the Human Brain Project, in collaboration with 3 international teams. May–Nov 12 Visitor Researcher at HCIL, UMD, Maryland. Under the advise of Ben Shneiderman and Catherine Plaisant. Involved in EventFlow project, a tool for the analysis of millions of event sequences. Publications 9 Published Most of them in JCR Q1 Journals like Neuroinformatics, Journal of Neuroscience or papers Frontiers in Neuroanatomy. H-Index 3. Google Scholar Conferences 6 Internat. Most of them are top congresses like IEEE VIS, ACM CHI or ECML PKDD. Lecturing conferences in three of them. http://bit.ly/juanmorales T SkypeID: juanmoralesdelolmo • H +34 608 029 224 B [email protected] 1/2 Computer skills Python Pandas, Numpy, Scipy, Matplotlib, JavaScript D3, Lodash, React, Angular, Back- VTK, ITK, Qt, Django, Flask, ZMQ, bone, jQuery, jQuery UI Gevent, Celery R Shiny, ggplot2, Statspat C++ Qt, VTK, ITK, STD, ZMQ Sys Admin Linux, Docker, Nginx, Apache Development Emacs, Eclipse, Qt Creator, R Studio, tools Git, SVN, CMake Database SQL, MongoDB Reporting LATEX, Sweave Languages Spanish First Language English Full professional Academic Experience 2014, 2015 Data Visualization, University Master in Graphical Computation and Simulation, U-tad, Madrid. -
References Cited [1] Salman Ahmad, Alexis Battle, Zahan Malkani, and Sepander Kamvar
References Cited [1] Salman Ahmad, Alexis Battle, Zahan Malkani, and Sepander Kamvar. The jabberwocky program- ming environment for structured social computing. In ACM User Interface Software and Technology (UIST), pages 53–64, 2011. [2] Saleema Amershi, James Fogarty, Ashish Kapoor, and Desney Tan. Overview based example se- lection in end user interactive concept learning. In ACM User Interface Software and Technology (UIST), pages 247–256, 2009. [3] Saleema Amershi, James Fogarty, Ashish Kapoor, and Desney Tan. Examining multiple potential models in end-user interactive concept learning. In ACM Human Factors in Computing Systems (CHI), pages 1357–1360, 2010. [4] Saleema Amershi, James Fogarty, and Daniel Weld. Regroup: Interactive machine learning for on- demand group creation in social networks. In ACM Human Factors in Computing Systems (CHI), pages 21–30, 2012. [5] Saleema Amershi, Bongshin Lee, Ashish Kapoor, Ratul Mahajan, and Blaine Christian. Cuet: Human-guided fast and accurate network alarm triage. In ACM Human Factors in Computing Systems (CHI), pages 157–166, 2011. [6] Apache lucene. http://lucene.apache.org/. [7] Alan R Aronson and Franc¸ois-Michel Lang. An overview of metamap: historical perspective and recent advances. Journal of the American Medical Informatics Association, 17(3):229–236, 2010. [8] David Bamman, Jacob Eisenstein, and Tyler Schnoebelen. Gender in twitter: Styles, stances, and social networks. CoRR, abs/1210.4567, 2012. [9] Michele Banko and Eric Brill. Scaling to very very large corpora for natural language disambiguation. In Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, ACL ’01, pages 26–33, 2001. [10] Lee Becker, George Erhart, David Skiba, and Valentine Matula. -
NIH-NSF Visualization Research Challenges Report
NIH-NSF Visualization Research Challenges Report Chris Johnson1, Robert Moorhead2, Tamara Munzner3, Hanspeter Pfister4, Penny Rheingans5, and Terry S. Yoo6 1 Scientific Computing and Imaging Institute School of Computing University of Utah Salt Lake City, UT 84112 [email protected] 2 Department of Electrical and Computer Engineering Mississippi State University Mississippi State, MS 39762 [email protected] 3 Department of Computer Science University of British Columbia 2366 Main Mall Vancouver, BC V6T 1Z4 Canada [email protected] 4 MERL - Mitsubishi Electric Research Laboratories 201 Broadway Cambridge, MA 02139 [email protected] 5 Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore, MD 21250 [email protected] 6 Office of High Performance Computing and Communications National Library of Medicine, U.S. National Institutes of Health Bethesda, MD 20894 [email protected] October 20, 2005 Contents 1 Executive Summary . 3 2 The Value of Visualization . 5 3 The Process of Visualization . 7 3.1 Moving Beyond Moore’s Law. 7 3.2 Determining Success . 9 3.3 Supporting Repositories and Open Standards . 10 3.4 Achieving Our Goals . 10 4 The Power of Visualization . 13 4.1 Transforming Health Care . 13 4.2 Transforming Science and Engineering . 16 4.3 Transforming Life . 19 5 Roadmap . 23 6 State of the Field . 25 6.1 Other Reports . 25 6.2 National Infrastructure . 25 6.2.1 Visualization Hardware . 26 6.2.2 Networking . 27 6.2.3 Visualization Software . 27 7 Workshop Participants 2 EXECUTIVE SUMMARY 1 In this report we describe some of the remarkable security, and competitiveness, support for research and achievements visualization enables and discuss the major development in this critical, multidisciplinary field has been obstacles blocking the discipline’s advancement.