Jiani Wang-Coldcode0214-Reflections

week10.md

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+Week 10
+---
+Link: https://sites.stat.washington.edu/wxs/Visualization-papers/focusing-and-linking.pdf
+
+Introduction
+---
+This paper discusses two basic principles for interactive visualization of high dimensional data: focusing and linking. 
+The paper and the accompanying video give examples of how graphical data analysis methods based on foucusing and linking are used in applications including linguistics, geographic informetion systems, time series analysis, and the analysis of multi-channel images arising in radiology and remote sensing.
+
+Focusing and linking are principles that offer a solution to the problem of visual overload. 
+Instead of maximizing the informetion in a single view, it is better to provide tools for quickly generating multiple views, each focussed on a different aspect of the data.
+Multiple views, however, should not be regarded partial information contained in individual views into a coherent image of the data as a whole. 

week11.md

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+Week 11
+---
+Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8070822
+
+Introduction
+---
+This paper enlightens us about the overview of Big Data, introduces the use of Augmented Reality and its concepts when it meets Big Data and Visualization.
+The paper also highlights the advantages, techniques of Big Data and illustrates the applications of Augmented Reality in various fields and domains.
+The 2D algorithms constructed by previous researchers for data visualization can be reconstructed into a 3D algorithm model which follows the principles of Augmented Reality.
+
+The paper describes the advantages of merging AR and BIG DATA to invent new interesting applications is starting to have a tangible presence.
+The main aim is to uncover the problems and ease the issues in visualization of Big Data and at the same time the objective of finding valid solutions for the problems in big data visualization remains.
+The sections mentioned below elaborates the present tools, techniques and platforms which cna be used for visualization in big data.
+Based on the results, a not so common approach is proposed: the capabilities and methods of virtual and augmented reality could be implemented to achieve visualization of the big data.
+
+The paper also discussed about the applications of AR and big data and fields where it is used. In the later sections, it also discussed about the user interface with presence of trending technologies, such as VR and AR diaplays on the big data visualization.
+

week12.md

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+Week 12
+---
+Link: https://ieeexplore.ieee.org/document/8457476
+
+Introduction
+---
+This paper focuses on a taxonomy of methods for evaluating uncertainty visualizationsUnderstanding. Evaluating the impact of an uncertainty visualization is complex due to the difficulties that people have interpreting uncertainty and the challenge of defining correct behavior with uncertainty information. 
+Currently, evaluators of uncertainty visualization must rely on general purpose visualization evaluation frameworks which can be ill-equipped to provide guidance with the unique difficulties of assessing judgments under uncertainty. 
+
+To help evaluators navigate these complexities, this paper present a taxonomy for characterizing decisions made in designing an evaluation of an uncertainty visualization. 
+Their taxonomy differentiates six levels of decisions that comprise an uncertainty visualization evaluation: the behavioral targets of the study, expected effects from an uncertainty visualization, evaluation goals, measures, elicitation techniques, and analysis approaches. 
+Applying our taxonomy to 86 user studies of uncertainty visualizations, we find that existing evaluation practice, particularly in visualization research, focuses on Performance and Satisfaction-based measures that assume more predictable and statistically-driven judgment behavior than is suggested by research on human judgment and decision making. 
+
+This paper reflect on common themes in evaluation practice concerning the interpretation and semantics of uncertainty, the use of confidence reporting, and a bias toward evaluating performance as accuracy rather than decision quality. 
+This paper conclude with a concrete set of recommendations for evaluators designed to reduce the mismatch between the conceptualization of uncertainty in visualization versus other fields.

week13.md

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+Week 13
+---
+Link: http://bth.diva-portal.org/smash/get/diva2:1486553/FULLTEXT02.pdf
+
+Introduction
+---
+This is a quite long paper which talks about evaluation of data visualization methods. 
+Due to the complex data and requirements, organizations often require effective visualization methods which impact business decisions and convince stakeholders. 
+This is a similar challenge in the development of a software
+demonstrator for the innovative safety radar system at ABB Jokab Safety whose aim
+is to improve the detection reliability using multiple radar sensors and requires an
+effective visualization method which will satisfy all the requirements.
+
+The main objective of this study is to explore different data visualization methods involved in illustrating the raw data and with the help of developers,
+and other team members feedback with reference to existing literature and filter them with respect to the system functionalities. 
+Establish evaluation criteria with relevant metrics to perform analytic evaluations on the visualization methods to determine an effective method.
+
+The results from this research provide insight into how data visualization evaluations can be implemented for real-time industrial problems 
+and furnish validation process to determine an effective data visualization method. 
+This study helps object detection using similar radar technologies visualize their data in an effective way 
+and provides a scientific approach for evaluating similar data visualization
+problems.

week14.md

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+Week 14
+---
+Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9307756
+
+Introduction
+---
+
+This paper first caught up my mind because of is graph at the beginning of the paper. The main idea about this paper is synchronous visualization. Because of the Covid-19, working from home being ‘the new normal’. This paper explored potential solutions for collaborating and prototyping remotely from our own homes using the existing tools at our disposal. 
+Since different teams have various technical skills, this paper used lots of synchronous remote design tools and methods. 
+This paper aimed to preserve the richness of colocated collaboration such as face-to-face physical presence, body gestures, facial expressions, and the making and sharing of physical artifacts. 
+While meeting over Zoom, the author sketched on paper and used digital collaboration tools, such as Miro and Google Docs. 
+The author articulate their challenges and strategies throughout the process, providing useful insights about synchronous distributed collaboration by using an auto-ethnographic approach.
+
+While descriptions and discussions of visualization design tended to assume co-located design teams, designers clearly need to carry out some work independently and in different locations. 
+However, there is important work to be done in considering these contexts for visualization design processes. 
+As a step towards developing a better understanding of distributed, synchronous visualization design, this paper provides an experiential understanding and conceptualization of this area.

week15.md

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+Week 15
+---
+Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8440816
+
+Introduction
+---
+This paper mainly discusses about animated sampling-oriented uncertainty visualizations. This paper identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. 
+The author contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. In a word, animated sampling-oriented uncertainty visualizations can support perceptual decision-making in a realistic context. 
+
+Animated representations of outcomes drawn from distributions are used in the media and other public venues to communicate uncertainty. 
+HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, 
+accurately, and automatically process the summary statistical properties of ensembles. 
+However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. 
+
+By modeling each participant’s accuracy as a function of the level of evidence presented over many repeated judgments, 
+the authors find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static
+aggregate uncertainty visualizations as a decision aid. 
+