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

The Alan Turing Institute (ATI)
The British Library, London
April 4, 2016 (All day) to April 6, 2016 (All day)

The British Library, London

  • Registration required
  • By invitation only

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

4-6 April 2016, The British Library, London

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:

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?
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?
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?
Preliminary 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 - 15:00 Welcome, opening, self-introduction, and keynote
15:00 - 15:30 Plenary brainstorming: Key Questions (a) and (b)
15:30 - 16:00 tea/coffee (and postie notes for continuing brainstorming)
16:00 - 17:10 Multi-disciplinary group discussion on Key Question (a)
17:10 - 17:30 Group reports
18:30 - 19:30 drink reception and networking
19:30 - 21:00 dinner

Session 2 (5 April AM)
9:00 - 10:30 Seven 8-minute short talks on Key Question (b) focusing on topics related to Computer Science and System Engineering, which is followed by a 30-minute plenary Q&A session with the seven speakers as the panellists.
10:30 - 11:00 tea/coffee
11:00 - 12:30 Seven 8-minute short talks on Key Question (b) focusing on topics related to Cognitive Sciences, which is followed by a 30-minute plenary Q&A session with the seven speakers as the panellists.
12:30 - 13:30 lunch

Session 3 (5 April PM)
14:00 - 15:30 Seven 8-minute short talks on Key Question (b) focusing on topics related to Mathematics, which is followed by a 30-minute plenary Q&A session with the seven speakers as the panellists.
15:30 - 16:00 tea/coffee
16:30 - 18:00 Seven 8-minute short talks on Key Question (b) focusing on other topics, which is followed by a 30-minute plenary Q&A session with the seven speakers as the panellists.
19:30 - 21:00 dinner

Session 4 (6 April AM)
9:00 - 10:30 Disciplinary-focused group discussions on Key Question (c)
10:30 - 11:00 tea/coffee
11:00 - 11:40 Disciplinary-based group report on Key Question (c), which is followed by a plenary Q&A session with the rapporteurs as the panellists.
11:40 - 12:30 Capstone, and 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