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Visual Analytics

Visual analytics is the science of analytical reasoning facilitated by visual interactive interfaces [1]. In our team we develop novel visual analytics frameworks, systems, and techniques that facilitate a range of end users to explore large data sets. Towards that end we follow the typical visual analytics process as shown in Figure 1.

Figure 1: The visual analytics process is characterised through interaction between data, visualisations, models about the data, and the users in order to discover knowledge [2].
Our particular focus is on the perceptual and cognitive aspects of a user that is engaging with a visual analytics system. We are essentially aiming to optimise the visual interface between humans and data with the aim to facilitate comprehension, reasoning, and informed decision making on complex data (see Figure 2). We take advantage of the fact that effective visualisation amplifies human cognitive capabilities by

  • increasing cognitive resources by using the visual resource to expand human working memory,
  • reducing search by representing large amounts of data in a small space, and
  • enhancing the recognition of patterns through effective spatio-temporal organisation.
Figure 2: The Visual Human-Data Interface.

While we are developing a broad capability in visual analytics, our current focus is on visual analytics of swarm sensing data manifested in the VizzzBees project. Other themes that we are currently exploring are

  • user experience in visual analytics through novel experiment design to assess user performance when working with interactive visual interfaces.
  • autonomous visual analytics recommending analytics and visualisation choices to improve interactive data exploration, effective information communication, and user experience.
  • context driven user querying aimed at improving meaningful information communication in a given context.
  • augmented reality interfaces, e.g. using smart glasses as depicted in Figure 3, that can provide an effective and exciting experience when visually exploring large data sets.
Figure 3: Smart glasses for augmented visual analytics of complex data (

[1] J. J. Thomas and K. A. Cook (Ed.), “Illuminating the Path: The R&D Agenda for Visual Analytics,” National Visualization and Analytics Center, p. 4, 2005.
[2] Ed. Keim et al., “Mastering the Information Age, Solving Problems with Visual Analytics”, 2010.