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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. -
An Interview with Visualization Pioneer Ben Shneiderman
6/23/2020 The purpose of visualization is insight, not pictures: An interview with visualization pioneer Ben Shneiderman The purpose of visualization is insight, not pictures: An interview with visualization pioneer Ben Shneiderman Jessica Hullman Follow Mar 12, 2019 · 13 min read Few people in visualization research have had careers as long and as impactful as Ben Shneiderman. We caught up with Ben over email in between his travels to get his take on visualization research, what’s worked in his career, and his advice for practitioners and researchers. Enjoy! Multiple Views: One of the main purposes of this blog is to explain to people what visualization research is to practitioners and, possibly, laypeople. How would you answer the question “what is visualization research”? Ben S: First let me define information visualization and its goals, then I can describe visualization research. Information visualization is a powerful interactive strategy for exploring data, especially when combined with statistical methods. Analysts in every field can use interactive information visualization tools for: more effective detection of faulty data, missing data, unusual distributions, and anomalies deeper and more thorough data analyses that produce profounder insights, and richer understandings that enable researchers to ask bolder questions. Like a telescope or microscope that increases your perceptual abilities, information visualization amplifies your cognitive abilities to understand complex processes so as to support better decisions. In our best -
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. -
The Eyes Have It: a Task by Data Type Taxonomy for Information Visualizations
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations Ben Shneiderman Department of Computer Science, Human-Computer Interaction Laboratory, and Institute for Systems Research University of Maryland College Park, Maryland 20742 USA ben @ cs.umd.edu keys), are being pushed aside by newer notions of Abstract information gathering, seeking, or visualization and data A useful starting point for designing advanced graphical mining, warehousing, or filtering. While distinctions are user interjaces is the Visual lnformation-Seeking Mantra: subtle, the common goals reach from finding a narrow set overview first, zoom and filter, then details on demand. of items in a large collection that satisfy a well-understood But this is only a starting point in trying to understand the information need (known-item search) to developing an rich and varied set of information visualizations that have understanding of unexpected patterns within the collection been proposed in recent years. This paper offers a task by (browse) (Marchionini, 1995). data type taxonomy with seven data types (one-, two-, Exploring information collections becomes three-dimensional datu, temporal and multi-dimensional increasingly difficult as the volume grows. A page of data, and tree and network data) and seven tasks (overview, information is easy to explore, but when the information Zoom, filter, details-on-demand, relate, history, and becomes the size of a book, or library, or even larger, it extracts). may be difficult to locate known items or to browse to gain an overview, Designers are just discovering how to use the rapid and Everything points to the conclusion that high resolution color displays to present large amounts of the phrase 'the language of art' is more information in orderly and user-controlled ways. -
Christopher L. North – Curriculum Vitae (Updated Sept 2014)
Christopher L. North – Curriculum Vitae (updated Sept 2014) Department of Computer Science (540) 231-2458 114 McBryde Hall (540) 231-9218 fax Virginia Tech north @ vt . edu Blacksburg, VA 24061-0106 http://www.cs.vt.edu/~north/ Google Scholar: • http://scholar.google.com/citations?user=yBZ7vtkAAAAJ • h-index = 35 Short Bio: Dr. Chris North is a Professor of Computer Science at Virginia Tech. He is Associate Director of the Discovery Analytics Center, and leads the Visual Analytics research group. He is principle architect of the GigaPixel Display Laboratory, one of the most advanced display and interaction facilities in the world. He also participates in the Center for Human-Computer Interaction, and the Hume Center for National Security, and is a member of the DHS supported VACCINE Visual Analytics Center of Excellence. He was awarded Faculty Fellow of the College of Engineering in 2007, and the Dean’s Award for Research Excellence in 2014. He earned his Ph.D. at the University of Maryland, College Park, in 2000. He has served as General Co-Chair of IEEE VisWeek 2009, and as Papers Chair of the IEEE Information Visualization (InfoVis) and IEEE Visual Analytics Science and Technology (VAST) Conferences. He has served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics (TVCG), the Information Visualization journal, and Foundations and Trends in HCI. He has been awarded over $6M in grants, co-authored over 100 peer-reviewed publications, and delivered 3 keynote addresses at symposia in the field. He has graduated 8 Ph.D. and 14 M.S. thesis students, 4 receiving outstanding research awards at Virginia Tech, and advised over 70 undergraduate research students including several award winners at Virginia Tech’s annual undergraduate research symposium. -
Human-Centered Artificial Intelligence: Three Fresh Ideas
AIS Transactions on Human-Computer Interaction Volume 12 Issue 3 Article 1 9-30-2020 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman University of Maryland, [email protected] Follow this and additional works at: https://aisel.aisnet.org/thci Recommended Citation Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Three Fresh Ideas. AIS Transactions on Human-Computer Interaction, 12(3), 109-124. https://doi.org/10.17705/1thci.00131 DOI: 10.17705/1thci.00131 This material is brought to you by the AIS Journals at AIS Electronic Library (AISeL). It has been accepted for inclusion in AIS Transactions on Human-Computer Interaction by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact [email protected]. Transactions on Human-Computer Interaction 109 Transactions on Human-Computer Interaction Volume 12 Issue 3 9-2020 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park, [email protected] Follow this and additional works at: http://aisel.aisnet.org/thci/ Recommended Citation Shneiderman, B. (2020). Human-centered artificial intelligence: Three fresh ideas. AIS Transactions on Human- Computer Interaction, 12(3), pp. 109-124. DOI: 10.17705/1thci.00131 Available at http://aisel.aisnet.org/thci/vol12/iss3/1 Volume 12 pp. 109 – 124 Issue 3 110 Transactions on Human-Computer Interaction Transactions on Human-Computer Interaction Research Commentary DOI: 10.17705/1thci.00131 ISSN: 1944-3900 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park [email protected] Abstract: Human-Centered AI (HCAI) is a promising direction for designing AI systems that support human self-efficacy, promote creativity, clarify responsibility, and facilitate social participation. -
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.