The Effect of Exploration Mode
and Frame of Reference in
Jorge Wagner, Wolfgang Stuerzlinger, Luciana Nedel (2021)
The design space for user interfaces for Immersive Analytics applications is vast. Designers can combine navigation and manipulation to enable data exploration with ego- or exocentric views, have the user operate at different scales, or use different forms of navigation with varying levels of physical movement. This freedom results in a multitude of different viable approaches. Yet, there is no clear understanding of the advantages and disadvantages of each choice. Our goal is to investigate the affordances of several major design choices, to enable both application designers and users to make better decisions. In this work, we assess two main factors, exploration mode and frame of reference, consequently also varying visualization scale and physical movement demand. To isolate each factor, we implemented nine different conditions in a Space-Time Cube visualization use case and asked 36 participants to perform multiple tasks. We analyzed the results in terms of performance and qualitative measures and correlated them with participants’ spatial abilities. While egocentric room-scale exploration significantly reduced mental workload, exocentric exploration improved performance in some tasks. Combining navigation and manipulation made tasks easier by reducing workload, temporal demand, and physical effort.
Comparing and Combining
Virtual Hand and Virtual Ray Pointer Interactions for Data Manipulation in Immersive Analytics
IEEE VR 2021 & IEEE TVCG
Jorge Wagner, Wolfgang Stuerzlinger, Luciana Nedel (2021)
In this work, we evaluate two standard interaction techniques for Immersive Analytics environments: virtual hands, with actions such as grabbing and stretching, and virtual ray pointers, with actions assigned to controller buttons. We also consider a third option: seamlessly integrating both modes and allowing the user to alternate between them without explicit mode switches. Easy-to-use interaction with data visualizations in Virtual Reality enables analysts to intuitively query or filter the data, in addition to the benefit of multiple perspectives and stereoscopic 3D display. While many VR-based Immersive Analytics systems employ one of the studied interaction modes, the effect of this choice is unknown. Considering that each has different advantages, we compared the three conditions through a controlled user study in the spatio-temporal data domain. We did not find significant differences between hands and ray-casting in task performance, workload, or interactivity patterns. Yet, 60% of the participants preferred the mixed mode and benefited from it by choosing the best alternative for each low-level task. This mode significantly reduced completion times by 23% for the most demanding task, at the cost of a 5% decrease in overall success rates.
Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration
IEEE VIS 2019 & IEEE TVCG
Jorge A. Wagner Filho, Wolfgang Stuerzlinger, Luciana Nedel (2019)
A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.
Comfortable Immersive Analytics with the VirtualDesk Metaphor: Case Studies and Perspectives
Jorge A. Wagner Filho, Carla M.D.S. Freitas, Luciana Nedel (2019)
VirtualDesk: A Comfortable and Efficient Immersive Information Visualization Approach
EuroVis 2018 & CGF
Jorge A. Wagner Filho, Carla M.D.S. Freitas, Luciana Nedel (2018)
3D representations are potentially useful under many circumstances, but suffer from long known perception and interaction challenges. Current immersive technologies, which combine stereoscopic displays and natural interaction, are being progressively seen as an opportunity to tackle this issue, but new guidelines and studies are still needed, especially regarding information visualization. Many proposed approaches are impractical for actual usage, resulting in user discomfort or requiring too much time or space. In this work, we implement and evaluate an alternative data exploration metaphor where the user remains seated and viewpoint change is only realisable through physical movements. All manipulation is done directly by natural mid-air gestures, with the data being rendered at arm’s reach. The virtual reproduction of the analyst’s desk aims to increase immersion and enable tangible interaction with controls and two dimensional associated information. A comparative user study was carried out against a desktop-based equivalent, exploring a set of 9 perception and interaction tasks based on previous literature and a multidimensional projection use case. We demonstrate that our prototype setup, named VirtualDesk, presents excellent results regarding user comfort and immersion, and performs equally or better in all analytical tasks, while adding minimal or no time overhead and amplifying user subjective perceptions of efficiency and engagement. Results are also contrasted to a previous experiment employing artificial flying navigation, with significant observed improvements.
Immersive Visualization of Abstract Information: An Evaluation on Dimensionally-Reduced Data Scatterplots
IEEE VR 2018
Jorge A. Wagner Filho, Marina F. Rey, Carla M.D.S. Freitas, Luciana Nedel (2018)
The use of novel displays and interaction resources to support immersive data visualization and improve analytical reasoning is a research trend in the information visualization community. In this work, we evaluate the use of an HMD-based environment for the exploration of multidimensional data, represented in 3D scatterplots as a result of dimensionality reduction (DR). We present a new modeling for this problem, accounting for the two factors whose interplay deter- mine the impact on the overall task performance: the difference in errors introduced by performing dimensionality reduction to 2D or 3D, and the difference in human perception errors under different visualization conditions. This two-step framework offers a simple approach to estimate the benefits of using an immersive 3D setup for a particular dataset. Here, the DR errors for a series of roll call voting datasets when using two or three dimensions are evaluated through an empirical task-based approach. The perception error and overall task performance, on the other hand, are assessed through a comparative user study with 30 participants. Results indicated that perception errors were low and similar in all approaches, resulting in overall performance benefits in both desktop and HMD-based 3D techniques. The immersive condition, however, was found to require less effort to find information and less navigation, besides providing much larger subjective perception of accuracy and engagement.
Immersive Analytics of Dimensionally-Reduced Data Scatterplots
IEEE VIS 2017 Workshop on Immersive Analytics
Jorge A. Wagner Filho, Marina F. Rey, Carla M.D.S. Freitas, Luciana Nedel (2017)
The brWaC Corpus: A New Open Resource for Brazilian Portuguese
Jorge A. Wagner Filho, Rodrigo Wilkens, Marco Idiart, Aline Villavicencio (2018)
In this work, we present the construction process of a large Web corpus for Brazilian Portuguese, aiming to achieve a size comparable to the state of the art in other languages. We also discuss our updated sentence-level approach for the strict removal of duplicated content. Following the pipeline methodology, more than 60 million pages were crawled and filtered, with 3.5 million being selected. The obtained multi-domain corpus, named brWaC, is composed by 2.7 billion tokens, and has been annotated with tagging and parsing information. The incidence of non-unique long sentences, an indication of replicated content, which reaches 9% in other Web corpora, was reduced to only 0.5%. Domain diversity was also maximized, with 120,000 different websites contributing content. We are making our new resource freely available for the research community, both for querying and downloading, in the expectation of aiding in new advances for the processing of Brazilian Portuguese.