The Alan Turing Institute (ATI) Symposium on
Theoretical Foundation of Visual Analytics

4-6 April 2016, The British Library, London

Summary Description   |   Scientific Questions   |   Key Topics   |   Programme   |   Organization

Summary Description:

Visual analytics, a term coined by Jim Thomas and his colleagues at the National Visualization and Analytics Center, has become the de facto standard process for integrating data analysis, visualization, and interaction to better understand complex systems. The necessity of integration rests on the following assertions:

  • Statistical Methods alone cannot convey an adequate amount of information for humans to make informed decisions—hence the need for Visualization;
  • Algorithms alone cannot encode an adequate amount of human knowledge about relevant concepts, facts, and contexts—hence the need for Interaction

  • Visualization alone cannot effectively manage levels of details about the data or prioritize different information in the data—hence the need for Statistical and Algorithmic Analysis and Interaction; and

  • Direct Interaction with data alone isn’t scalable to the amount of data available—hence the need for Statistical and Algorithmic Analysis and Visualization.

These four assertions apply to any data intelligence process for complex phenomena and environments. While there are established theoretical foundations for statistics and algorithms (including machine-learned algorithms), and emerging theories for visualization and interaction, there is not yet a theoretical foundation for underpinning all four components in a coherent manner. It will be highly desirable for ATI to initiate the development of such a unified theoretical foundation. Historically theoretical unification has always been a driving force in the advancement of the sciences. Alan Turing’s Universal Computer exemplifies such a great endeavour.

The objective of this symposium is to outline the scope of such a theoretical foundation; identify the known theoretical components, and assess their role in underpinning each of the four components; envision the development paths in the coming years through collective effort of different disciplines. (e.g., computer science (visual analytics, HCI, artificial intelligence, ...), mathematics, cognitive sciences, engineering, social sciences, and so on).

Key Scientific Question to be Answered:

  1. What would a theory of visual analytics be able to do? What phenomena may it explain, what measurements may it feature, what laws may it derive, what causal relationships may it model, and what outcomes may it predict?
    Example: Why doesn’t entropy maximization usually result in the best visual design in visualization?
  2. What existing theories in mathematics, computer science, cognitive sciences, and other disciplines may contribute to the theoretical development in visual analytics? What are their strengths and weaknesses in relation to the four components of visual analytics (i.e., statistics, algorithms, visualization and interaction), and to the requirements in (a) (i.e., explanation, measurement, laws, causality, and prediction)?
    Example: How do gestalt effects benefit visual analytics, and how can such benefits be quantitatively measured?
  3. What would be possible pathways that may lead to the establishment of such a theoretical foundation, and what would be the milestones for measuring successes in research and development.
    Example: What is the best way to utilise the capability of interactive visualization in breaking the conditions of data processing inequality, which is a major theoretical and practical stumbling block in data intelligence?

Key Topics to be Addressed:

The symposium focuses on Visual Analytics, which naturally brings computer science, mathematics, cognitive sciences, and many other disciplines together. A theoretical foundation for visual analytics will include concepts, measures, models, and quantitative laws (and theorems) for important notions. The symposium is expected to raise many scientific questions to be addressed. Below is a small subset of example questions identified by the organisers.

  • How to measure information and knowledge in data processing, analysis and visualization?
  • How to measure or estimate cost-benefit of machine-centred components and human-centred activities in a visual analytics workflow, and how to optimise such a workflow?
  • When the conditions of data processing inequality are broken (e.g., by human-centred activates), how to measure or estimate the extra information in the data processing pipeline?
  • How to measure or estimate the uncertainty in a data processing pipeline, including that in the source data, introduced by the machine-centred processing components and human-centred activities?

Provisional Programme:

The symposium will have a range of activities organised into four half-day sessions. We plan to hold the symposium over a three day period (i.e., from 14:00 on day 1 to 12:30 on day 3) as it suits the UK participants generally better. Below is a provisional schedule for the four half-day sessions:

Session 1 (4 April PM)

  • 11:00 - 14:00    Registration and buffet lunch from 12:30
  • 14:00 - 14:30    Welcome: Dame Lynne Brindley DBE
                       followed by self-introduction by attendees
  • 14:30 - 15:30    Keynote: Bradley Love, Experimental Psychology, UCL
                       "People's Inductive Biases in Learning and Decision Making"
  • 15:30 - 15:50    tea/coffee (and postie notes for continuing brainstorming)
  • 15:50 - 16:30    Short Presentations (20min), followed by plenary Q&A (20min):
                       Session Chair: Jason Dykes
                       Georges Grinstein
    : "Why integrating perceptual, cognitive and other human models is critical in a visual analytics theory"
                       Alan Blackwell: "Theoretical foundations of user experience in visual analytics"
  • 16:30 - 17:20    Group Discussion: Key Questions (1) and (2)
  • 17:20 - 17:30    Group Reports
  •    
  • 19:30 - 21:00    Dinner (Thistle Euston Hotel, Cardington Street, Euston London NW1 2LP)

Session 2 (5 April AM)

  • 09:00 - 10:00   Short Presentations (40min), followed by plenary Q&A (20 minutes):
                      Session Chair: MIn Chen
                      Natalia Andrienko
    : "Role of data transformations in visual analytics"
                      Rita Borgo: "Glyph-based visualization: strengths and shortcomings"
                      Jonathan C. Roberts Visual Analytics interaction and investigation beyond traditional WIMP"
                      Robert S Laramee: "Can theory from sorting contribute to visual analytics theory?"
  • 10:00 - 10:30    Disciplinary-focused Group Discussion: "Can any existing theory underpin all four components of visual analytics?" 
  • 10:30 - 10:50    tea/coffee
  • 10:50 - 11:20    Disciplinary-focused Group Discussion: "Can any existing theory underpin all four components of visual analytics?"
  • 11:20 - 11:30    Group Reports
  • 11:30 - 12:30    Plenary Panel: "Is it realistic to build a theoretical foundation for visual analytics, and if so, how?"
                      Panellists: Michal Branicki, Bryan Edwards, Amos Golan, Chris Hankin, Anne Trefethen
                     
    Panel Moderator: Helen C. Purchase
  • 12:30 - 13:30    lunch

Session 3 (5 April PM)

  • 14:00 - 15:00    Short Presentations (40min), followed by plenary Q&A (20 minutes):
                      Session Chair: Daniel Archambault
    Niloy Mitra
    : "Discovering structures in 3D model collections -- visual computing meets machine learning"
                      Mark W. Jones
    : "Framework4 -- A visual analytics system for classifying multi-dimensional time series data"
                      Gennady Andrienko: "How to integrate cluster analysis with interactive visualization?"
                      B. L. William Wong: "Human Factors principles and concepts that underpin designs for sense-making in visual analytics"
                      Hamish Carr: "In situ topological analysis"
  • 15:00 - 15:30    Interdisciplinary Group Discussion: "Does visual analytics inspire a unified theoretical framework to underpin all four components?"
  • 15:30 - 15:50    tea/coffee
  • 15:50 - 16:20    Interdisciplinary Group Discussion: "Does visual analytics inspire a unified theoretical framework to underpin all four components?"
  • 16:20 - 16:30    Group Reports
  • 16:20 - 17:30    Plenary Panel: "Does visual analytics need a theoretical foundation, and will it benefit practical applications?"
                      Panellists: David Duce, Alex Healing, Eric T. Meyer, Rob Procter, Andrew Prescott
                     
    Panel Moderator: Peter Robinson

Session 4 (6 April AM)

  • 09:00 - 10:00    Plenary Panel: "If there were a unified theoretical framework, what would be its impact on the status quo of statistics, algorithms, visualization and interaction?"
                      Panellists: Alan Blackwell, Michal Branicki, Min Chen, Georges Grinstein
                      Panel Moderator: Jason Dykes
  • 10:00 - 10:30    Interdisciplinary Group Discussion: Key Question (3)
  • 10:30 - 10:50    tea/coffee
  • 10:50 - 11:20    Interdisciplinary Group Discussion: Key Question (3)
  • 11:20 - 11:30    Group Reports
  • 11:30 - 12:25    Capstone: David Ebert, Purdue University, USA
                       "Evolving Foundations and Understanding of Visual Analytics Theory"
  • 12:25 - 12:30    Closing.
  • 12:30 - 13:30    lunch

Organisation:

Academic Organisers:
  • Professor Min Chen, University of Oxford
  • Professor Anthony Steed, UCL
  • Professor Ralph Schroeder, University of Oxford
Event Manager:
  • Clementine Hadfield