Ph.D
Group : Human-Centered Computing

A Content-Aware Design Approach to Multiscale Navigation

Starts on 01/09/2010
Advisor : PUECH, Claude
[Emmanuel Pietriga, Olivier Chapuis]

Funding :
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 20/12/2013, committee :

Research activities :

Abstract :
Computer screens are very small compared to the size of large information spaces that arise in many domains. The visualization of such datasets requires multiscale navigation capabilities, enabling users to switch between zoomed-in detailed views and zoomed-out contextual views of the data. Designing interfaces that allow users to quickly identify objects of interest, get detailed views of those objects, relate them and put them in a broader spatial context, raise challenging issues. Multi-scale interfaces have been the focus of much research effort over the last twenty years.

There are several design approaches to address multiscale navigation issues. In this thesis, we review and categorize these approaches according to their level of content awareness. We identify two main approaches: content-driven, which optimizes interfaces for navigation in specific content; and content-agnostic, that applies to any type of data. We introduce the content-aware design approach, which dynamically adapts the interface to the content. The latter design approach can be used to design multiscale navigation techniques both in 2D or 3D spaces. We introduce Arealens and Pathlens, two content-aware fisheye lenses that dynamically adapt their shape to the underlying content to better preserve the visual aspect of objects of interest. We describe the techniques and their implementation, and report on a controlled experiment that evaluates the usability of Arealens compared to regular fisheye lenses, showing clear performance improvements with the new technique for a multiscale visual search task. We introduce a new distortion-oriented presentation library enabling the design of fisheye lenses featuring several foci of arbitrary shapes. Then, we introduce Gimlens, a multi-view detail-in-context visualization technique that enables users to navigate complex 3D models by drilling holes into their outer layers to reveal objects that are buried into the scene. Gimlens adapts to the geometry of objects of interest so as to better manage visual occlusion problems, selection mechanism and coordination of lenses.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.