Does Visualization Speed Up the Safety Analysis Process?

AlTarawneh, Ragaad; Steiner, Max; Taibi, Davide; Humayoun, Shah Rukh; Liggesmeyer, Peter

doi:10.1007/978-3-319-10557-4_47

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later, 2D plus 3D visual interactive environment called ESSAVis++ introduced in [22] also adopt this kind of graph extraction technique to help users navigate through the graph representation of the failure mechanism. A visualization tools comparison study conducted in [23] show that visual-based tools really help in analyzing the common fault tree models more accurately and efficiently. The method of compound graphs is also employed in [24] for the bio-molecular interaction networks along with force-directed layout, where the networks are placed into cellular components to quickly identify the location of the network elements, especially for the densely populated locations.…”

Section: Related Workmentioning

confidence: 99%

VizTract: Visualization of Complex Social Networks for Easy User Perception

Akula

Garibay

2019

BDCC

View full text Add to dashboard Cite

Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a two-level graph abstraction visualization tool that is designed to visualize both hierarchical and adjacency information in a tree structure. We use the Facebook dataset from the Social Network Analysis Project from Stanford University. On this data, social groups are referred as circles, social network users as nodes, and interactions as edges between the nodes. Our approach is to present a visual overview that represents the interactions between circles, then let the user navigate this overview and select the nodes in the circles to obtain more information on demand. VizTract aim to reduce visual clutter without any loss of information during visualization. VizTract enhances the visual perception of complex social networks to help better understand the dynamics of the network structure. VizTract within a single frame not only reduces the complexity but also avoids redundancy of the nodes and the rendering time. The visualization techniques used in VizTract are the force-directed layout, circle packing, cluster dendrogram, and hierarchical edge bundling. Furthermore, to enhance the visual information perception, VizTract provides interaction techniques such as selection, path highlight, mouse-hover, and bundling strength. This method helps social network researchers to display large networks in a visually effective way that is conducive to ease interpretation and analysis. We conduct a study to evaluate the user experience of the system and then collect information about their perception via a survey. The goal of the study is to know how humans can interpret the network when visualized using different visualization methods. Our results indicate that users heavily prefer those visualization techniques that aggregate the information and the connectivity within a given space, such as hierarchical edge bundling.

show abstract

Section: Related Workmentioning

confidence: 99%