Multiple Workspaces in Visual Analytics

Meseery, Maha El; Wu, Yuyao; Stuerzlinger, Wolfgang

doi:10.1109/bdva.2018.8534019

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…The study presented in [42], focused on VA techniques and developed a prototype to investigate the benefits of having multiple workspace capability in a VA system. The authors argued that having the ability to create multiple workspaces in a tab-based interface, provides users the ability to follow multiple analysis paths and pursue different hypothesis simultaneously.…”

Section: ) Plan Processesmentioning

confidence: 99%

“…However, in the conventional visualization systems such as those presented in [44] and [41], data visualization is done once and any modification to the data filtering or dimension selection alters the overall visuals and previous analysis paths will be lost. This issue is addressed in another study [42], where multiple concurrent workspaces are proposed as a solution for back tracking of previous analysis paths. In this way, users can see multiple outcomes of their work and analyze different aspects of the analysis at the same time.…”

Section: ) Plan Processesmentioning

confidence: 99%

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Visual Analytics for Decision Support: A Supply Chain Perspective

2021

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Supply chain (SC) activities generate huge amount of data that can be used in decision making processes. However, proper data analytics techniques are required to combine, organize, and analyze data from different sources and produce required insights available for decision makers. These techniques promote analytical reasoning of the events and patterns hidden in the data using visualizations, so-called Visual Analytics (VA). Although there is a large number of VA systems to facilitate the process of analysis and decision making, there is a lack of an adequate overview of what already exists in this area for SC management. To address that need, we conducted a systematic literature review to analyze the state of the art in SC VA systems. Particularly, we focus on use cases, the type of the decisions that a VA system intended to support, the type of visualizations employed, the type of analytics used, and the data that has been used for analysis. The goal of this study is to provide SC and VA researchers with an overview of the works carried out in the field of SC VA, helping them to observe latest trends and to recognize existing gaps that need further investigation. Consequently, a mapping between decisions of various SC business processes and their reciprocal visualization techniques and tactics have been provided. Adding to that, VA applications and use cases in SC are identified based on the SC Operation Reference (SCOR) model and underlying decision areas are recognized.

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Section: ) Plan Processesmentioning

confidence: 99%

Section: ) Plan Processesmentioning

confidence: 99%

Visual Analytics for Decision Support: A Supply Chain Perspective

2021

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“…We further used a VA tool called DynSpace to carry out analytical tasks. DynSpace runs as a web application on a Node.js server with MongoDB running in the backend to support data collection during experiments [33]. We chose a web application because of its ubiquitous accessibility and flexibility in terms of both client and server side technologies and to ease for future expandability on large display screens.…”

Section: Apparatusmentioning

confidence: 99%

Immersive Analytics Sensemaking on Different Platforms

Blum¹,

Cetin²,

Stuerzlinger³

2019

Computer Science Research Notes

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In this work we investigated sensemaking activities on different immersive platforms. We observed user s during a classification task on a very large wall-display system (experiment I) and in a modern Virtual Reality headset (experiment II). In experiment II, we also evaluated a condition with a VR headset with an extended field of view, through a sparse peripheral display. We evaluated the results across the two studies by analyzing quantitative and qualitative data, such as task completion time, number of classifications, followed strategies, and shape of clusters. The results showed differences in user behaviors between the different immersive platforms, i.e., the very large display wall and the VR headset. Even though quantitative data showed no significant differences, qualitatively, users used additional strategies on the wall-display, which hints at a deeper level of sensemaking compared to a VR Headset. The qualitative and quantitative results of the comparison between VR Headsets do not indicate that users perform differently with a VR Headset with an extended field of view.

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