Hello everyone, the title of our paper is “Hybrid Line-Based and Region-Based Interactive Set Data Visualization”.
The authors of this paper are Xiaohan Wang, Chuyu Zhang , Yu Zhu, Xueyi Chen, Liming Shen, Richen Liu*, and Rongtao Qian from Nanjing Normal University.
Nowadays, it is challenging to visualize the complex set information.
For example, in survey writing, one literature may belong to multiple categories while one category may involve multiple literatures.
The traditional tabular form is not intuitive.
The line-based scheme is lack of a representative image, while the region-based scheme generates artefacts caused by empty overlapping regions.
The most related work includes three categories.
Venn or Euler diagrams represent set membership by closed curves.
Region-based methods use a closed curve surrounding the set items to define a region.
Line sets generates a line to represent each set.
We proposed four design goals.
Also, when users select a category, balloons and lines of that category are highlighted.
Users can drag items to re-layout to avoid too much visual clutter.
First, we use blob algorithm.
The red point O represents the central point.
The color of orange represents the start state and the color of blue represents the final state.
Second, the reason why the order of connection is based on the cost rather than distance is that the cost can not only make the path simpler but also pass less obstacles.
To avoid line connection, the control points A and B are used to bypass the blue balloons.
The orange lines are the initial connection and the red lines are the connection after route algorithm.
We design a hierarchical clustering algorithm to merge balloons which can further reduce the visual clutter.
We define and calculate the distance of each pair of balloons.
We recruited 12 participants. They identify literatures cited in survey paper by three methods respectively.
We obtain the comparative timing results of three methods.
Also, we design a questionnaire of 9 questions and record the results.
Almost all of the questionnaire results are positive.
We conduct case study on different scales of literature datasets collected from three different survey papers.
First, survey on emerging trends by deep learning method.
Second, the survey on simulation data visualization.
Third, survey on social media data visualization.
We compare the original tabular form, line-based Kelp Diagram, region-based BubbleSets, and our method.
The results show our method alleviates artefacts and has representative images.
We evaluate our method by another two cases on ensemble data to reveal the temporal change correlations across simulation runs.
It shows our method can visualize set information in the overlapping clusters and overlapping simulation runs and can visualize the evolution of data behaviors in simulation data.
There are some limitations in our method.
First, when the number of set data items is too large, our tool will either introduce visual clutter, or make the representative images too small to perceive.
Second, it is hard to perceive the colors when there are too many set categories.
In the future, we plan to enable users better control the merging processes interactively when hierarchical merging.
BalloonVis is a novel method to explore set information by using a line-based and region-based hybrid strategy.
It could support the placement of representative illustration for each item, and the reduction of visual clutters.
Its scalability is guaranteed by a hierarchical merging optimization to further eliminate the clutters.
We would like to thank the reviewers for their valuable comments and appreciate all the domain experts and user study participants.
This work was supported by the following two fundings.
Thank you for your attention!