Rationale Visualization for Safety and Security

Scheepens, Roeland; Michels, Steffen; Wetering, Huub van de; Wijk, Jarke J. van

doi:10.1111/cgf.12631

Cited by 9 publications

(4 citation statements)

References 22 publications

(39 reference statements)

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“…Similarly, SensePath [NXW * 16], SenseMap [NXB * 16], and InsideInsights [MHK * 19] provide webbased interfaces for the analysis and exploration of provenance data. These techniques further allow for the exploration of large provenance data collections, which can benefit safety and security tasks [SMvdWvW15] as well as scientific research [GLG * 16]. The analysis can also be collaborative, as seen in the study using RCloud by North et al [NSUW15].…”

Section: Analysis Via Visual Analyticsmentioning

confidence: 99%

Survey on the Analysis of User Interactions and Visualization Provenance

Xu¹,

Ottley²,

Walchshofer³

et al. 2020

Preprint

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There is fast-growing literature on provenance-related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence-based guidelines and highlights concrete opportunities for future development in this emerging area.

show abstract

Section: Analysis Via Visual Analyticsmentioning

confidence: 99%

Survey on the Analysis of User Interactions and Visualization Provenance

Xu¹,

Ottley²,

Walchshofer³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, SensePath [NXW*16], SenseMap [NXB*16], and InsideInsights [MHK*19] provide web‐based interfaces for the analysis and exploration of provenance data. These techniques further allow for the exploration of large provenance data collections, which can benefit safety and security tasks [SMvdWvW15] as well as scientific research [GLG*16]. The analysis can also be collaborative, as seen in the study using RCloud by North et al [NSUW15].…”

Section: Techniques: How To Analyze Provenance Datamentioning

confidence: 99%

Survey on the Analysis of User Interactions and Visualization Provenance

Ottley

Walchshofer

et al. 2020

Computer Graphics Forum

View full text Add to dashboard Cite

There is fast‐growing literature on provenance‐related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence‐based guidelines and highlights concrete opportunities for future development in this emerging area. The survey and papers discussed can be explored online interactively at https://provenance-survey.caleydo.org.

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“…In other scenarios, the only available data is the steps performed by the model. The work of Scheepens et al [SMvdWvW15] aims at visualizing the rationale of a reasoning engine that is fed with possibly unreliable sources. Due to the nature of unreliability, experts require a support system to discard possible false alarms.…”

Section: Related Workmentioning

confidence: 99%

V‐Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Models

Caballero

Westenberg

Gebre

et al. 2019

Computer Graphics Forum

Self Cite

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The usage of deep learning models for tagging input data has increased over the past years because of their accuracy and high‐performance. A successful application is to score sleep stages. In this scenario, models are trained to predict the sleep stages of individuals. Although their predictive accuracy is high, there are still mis classifications that prevent doctors from properly diagnosing sleep‐related disorders. This paper presents a system that allows users to explore the output of deep learning models in a real‐life scenario to spot and analyze faulty predictions. These can be corrected by users to generate a sequence of sleep stages to be examined by doctors. Our approach addresses a real‐life scenario with absence of ground truth. It differs from others in that our goal is not to improve the model itself, but to correct the predictions it provides. We demonstrate that our approach is effective in identifying faulty predictions and helping users to fix them in the proposed use case.

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Rationale Visualization for Safety and Security

Cited by 9 publications

References 22 publications

Survey on the Analysis of User Interactions and Visualization Provenance

Survey on the Analysis of User Interactions and Visualization Provenance

Survey on the Analysis of User Interactions and Visualization Provenance

V‐Awake: A Visual Analytics Approach for Correcting Sleep Predictions from Deep Learning Models

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