Keynote Speaker 5
| Title | Thinking Landscapes |
| Speaker | Dr. William Wong, Middlesex University's School of Science and Technology |
Biography
Dr. William Wong is Professor of Human-Computer Interaction and Head, Interaction Design Centre, at Middlesex University's School of Science and Technology, London, UK. Prior to academia, Professor Wong worked in the Republic of Singapore Air Force in a number of roles, including as an Air Defence Controller directing fighters and missile systems, and then as Head, Systems and Communications Operations Branch, HQ RSAF. His research interest is in the representation design of information to support decision making in naturalistic environments.
From a Cognitive Work Analysis perspective, his research has included air traffic control, hydro- electricity dispatch control, emergency ambulance command and control, intelligence analysis, and visual analytics, with the view of developing user interfaces that enhance information uptake and support decision making and situation awareness in real-time dynamic environments. He is recipient of over US$7.1 million in grants, and has been project coordinator for several US-UK and European Union multi- institution R&D project consortiums. Together with his students and colleagues, he has published over 100 scientific peer reviewed articles.
Abstract
On a thinking landscape, space and place have meaning, and by spatially arranging information across the thinking landscape, it is possible to enhance awareness, support familiar information interaction behaviours and strategies, and by making conclusion pathways visible, we can encourage collaboration. By way of examples, we discuss the challenges for designing visual analytics environments for supporting human reasoning and the construction of meaning and explanations. Visual analytics is the science of analytic reasoning facilitated by interactive visualisation coupled with computation functionality.
We discuss the concept of a ReasoningWorkspace where analysts can discover what is in their datasets, analyse the data, and to then assemble relevant pieces of information together to construct meaningful explanations. Unlike engineered causal systems such as hydro-power plants, the processes in intelligence analysis systems do not have similar a priori functional relationships for which to visually represent. Instead, in domains such as intelligence analysis, a key challenge is to discover these important relationships from seemingly un-connected information, assemble them in unique organisations that enable the construction of meaningful explanations.
Thinking about reasoning workspaces as a landscape affords spatialization of information and their representation as objects that can be manipulated to assemble and construct into arguments. Some of these arguments would be very tentative and at an early stage and potentially based on missing, unavailable or unreliable data, focusing on gaining traction, requiring the analyst to fluidly express his thinking as he transitions between creative exploration and finally settling down to more formal and rigorous arguments.
