About Aaron Quigley

Professor Aaron Quigley is the Chair of Human Computer Interaction in the School of Computer Science, University of St Andrews, Scotland. He directs the St Andrews Computer Human Interaction research group (SACHI) and is one of the theme leaders for the Multimodal Interaction research theme in SICSA. the Scottish Informatics and Computer Science Alliance. His research interests include surface and multi-display computing, human computer interaction, pervasive and ubiquitous computing and information visualisation. Aaron has published over 115 internationally peer-reviewed publications including edited volumes, journal papers, book chapters, conference and workshop papers and holds 3 patents. He has held academic and industry appointments in Australia, Japan, the USA, Germany, Ireland and the UK. He is the Editor-In-Chief for the Journal "Computers", a member of the joint steering committee for UbiComp and Pervasive and has had chairing roles in twenty international conferences and has served on over eighty conference and workshop program committees.

The question is key in Trading Consequences

“Dreams are today’s answers to tomorrow’s questions.”
– Edgar Cayce

Looking back to the global trading of commodities during the 19th century we see increasing access to digitised historical record, in a myriad of forms. Today, the rate at which we can collect and store data about trading is ever expanding, from high level statistics, to low level sensor data on containers in transit. In each case, the scale of the data is rapidly outstripping the provision of tools for the effective analysis and exploration of such data. The volume of data results in historians focussing on popular commodities or analysts asking for course-grained, aggregate measures.

Image of Savannah ©iStockPhoto 2012

Instead, to understand the consequences of our trading history, historians need to ask difficult, subtle, multifaceted and challenging questions. Questions which aren’t polluted by knowledge of the limitations of the methods and technologies we have today. These insightful questions won’t come from a focus on what the tools of today can support, what the analysis or visualisation methods can do or what data is available. Simply put, if you only know about hammers, all your problems will look start to like nails. And worse than this, everyone will start to think like the carpenter, reducing the power that the breadth of inter-disciplinary expertise gives you.

Overview of Information Visualisation pipeline

Figure 1: Overview of Information Visualisation pipeline

In this project we are bringing together an inter-disciplinary research team of historians, text analysis and information visualisation experts. Instead of starting with the key “historical questions” which historians are seeking answers to, it’s very tempting to focus on one or more of the earlier technology stages as shown in Figure 1. This figure is our adapted view of the “information visualisation pipeline” [1,2].¬†Data comes in a variety of abstract forms without a clear physical manifestation and needs to be dynamically collected, processed, cleaned and hence mined before interactive display.

However, if we first focus on a technology stage it will impact on the questions the historians might be able ask or the approaches to be taken. Consider, for example, the rendering step in Figure 1. Modern graphics APIs (eg. OpenGL), desktop computers or even commodity displays are showing increased ease of access to 3D software and hardware. If this is our starting point, we can quickly see how 3D stereoscopic tools will emerge and will shape what (if any) questions historians might pose, with our tools.

Focussing first on the data, mining, software, algorithms, layouts, methods etc. is the wrong approach in a project such as Trading Consequences. Instead, the historical questions are key. Our challenge as a team is to ensure that at the earliest stage we do not pollute the aspirations of historians. We need to encourage the historians to ask interesting questions about the data, without being hampered by expectations of what is feasible given current technology.

Of course, over time as questions emerge, prototypes will be developed and the creation of a shared view across a team is natural. We aim to continually bring in fresh perspectives to ensure that we are answering the questions which need to be asked, rather than the questions which can be asked.

[1] Stuart T. Kard, Jock D. Mackinlay, Ben Scheiderman (1999) Readings in Information Visualization: Using vision to think. Morgan Kaufman.
[2] Ben Fry (2007), Visualizing Data: Exploring and Explaining Data with the Processing Environment. O’Reilly Media.