Node Overlap Removal for 1D Graph Layout

Fadloun, Samiha; Poncelet, Pascal; Rabatel, Julien; Roche, Mathieu; Sallaberry, Arnaud

doi:10.1109/iv.2017.14

Cited by 3 publications

(1 citation statement)

References 22 publications

(35 reference statements)

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“…When several nodes are merged by the user, we position the resulting node at the barycenter of the merged nodes. Then, we use a node overlap removal algorithm 37 to arrange the layout on the axis while preserving the relative distances between the consecutive pairs of nodes. 38 The user can also add links between the nodes to model keyword relationships.…”

Section: Epidvis Designmentioning

confidence: 99%

EpidVis: A visual web querying tool for animal epidemiology surveillance

Fadloun

Sallaberry

Mercier

et al. 2019

Information Visualization

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The use of electronic media for the detection and monitoring of animal disease outbreaks is crucial for disease surveillance and early warning systems. Animal health specialists regularly query web pages using various formulations to obtain up-to-date news on disease outbreaks. This task, however, is often manual and time-consuming. Visualization techniques can nevertheless facilitate their web searches, compared to traditional searches. This article presents EpidVis, a visual web query tool designed for experts in animal health, conducting epidemic intelligence activities from news sources on the Internet. It consists of several views that help the domain experts efficiently build and launch queries, as well as visualize the results. Moreover, it supports external information integration to help domain experts enrich their knowledge and adapt their queries. EpidVis was assessed considering usability (user study) and usefulness for experts (case study). The results show that our tool helps domain experts in their daily surveillance tasks, allowing them to extract in timely manner accurate information on disease outbreaks from the web.

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Section: Epidvis Designmentioning

confidence: 99%

EpidVis: A visual web querying tool for animal epidemiology surveillance

Fadloun

Sallaberry

Mercier

et al. 2019

Information Visualization

Self Cite

View full text Add to dashboard Cite

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CircleVis: A Visualization Tool for Circular Labeling Arrangements and Overlap Removal

2022

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Information visualization refers to the practice of representing data in a meaningful, visual way that users can interpret and easily comprehend. Geometric or visual encoding shapes such as circles, rectangles, and bars have grown in popularity in data visualization research over time. Circles are a common shape used by domain experts to solve real-world problems and analyze data. As a result, data can be encoded using a simple circle with a set of labels associated with an arc or portion of the circle. Labels can then be arranged in various ways based on human perception (easy to read) or by optimizing the available space around the circle. However, overlaps can occur in one or more arrangements. This paper proposes CircleVis, a new visualization tool for label arrangement and overlap removal in circle visual encoding. First, a mathematical model is presented in order to formulate existing arrangements such as angular, path, and linear. Furthermore, based on user interaction, a new arrangement approach is proposed to optimize available space in each circle arc and delete label overlaps. Finally, users test and evaluate the designed tool using the COVID-19 dataset for validation purposes. The obtained results demonstrate the efficacy of the proposed method for label arrangement and overlapping removal in circular layout.

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