Kamran Sedig (short bio)
Department of Computer Science, Western University, Canada
Paul Parsons (short bio)
Department of Computer Graphics Technology, Purdue University, USA
The objective of this tutorial is to provide attendees with a powerful conceptual framework to help with interaction design for visualizations. We will discuss some empirical studies that have implications for design. We will also identify a number of principles and guidelines that can be used in the design and evaluation of interactive visualizations. The attendees will learn how to design interaction and interactivity in the context of performing cognitive activities with visual data.
Visualizations allow users to engage with data and concepts to perform cognitive tasks and activities such as analyzing climate change patterns, making sense of public health data, selecting machine learning models, and interpreting financial data. Data and concepts underlying visualizations often have deep, hidden, latent, and layered meaning and structure. Making visualizations interactive is imperative for supporting cognitive activities. In other words, visualization tools should be designed such that users can engage in an ongoing discourse with the underlying information as they carry out different tasks.
In this tutorial, we will discuss the relationships between cognitive activities, tasks, and interactions in the context of working with visualizations. We will present several components of EDIFICE to help with the systematic design of interactions for visualizations. Some topics that will be covered include: 1) the ontological aspects of interaction—e.g., what interactions exist, at what level of abstraction interactions can be identified and characterized, what properties of individual interactions can be identified, and how interactions can be categorized; 2) the operational aspects of interaction—e.g., how interactions can and should be put into operation; and 3) the hierarchical relationships among cognitive activities, tasks, and actions.
Researchers, graduate students, and industry practitioners who are interested in creating highly interactive visualizations to analyze and interpret data in different domains--e.g., health informatics, scientific exploration, and data analytics.