Evaluation of Information Visualization Tools Using the NFR Approach

Author(s):  
Pushpa Kumar ◽  
Nary Subramanian ◽  
Kang Zhang
2021 ◽  
Vol 7 (1) ◽  
pp. 283-292
Author(s):  
Rosaura Fernández-Pascual ◽  
Ana Marín Jiménez ◽  
María Pilar Fernández- Sánchez

This paper explores how to incorporate information visualization tools into qualitative studies to represent the underlying structure of knowledge. Information visualization plays a key role in many areas such as decision-making, data mining, market studies, or knowledge management. A case of experiential learning was developed for Quantitative Techniques in Business and Administration and Economy Degrees at the University of Granada, Spain. The goal is to analyze the opinion of students (n = 227) on the development of the activity through information visualization techniques. The gathered information was subjected to a categorization process to unify and homogenize the responses. After a term-clumping process, a co-word analysis using the VosViewer software is used to analyze the relationships among terms and provide the network maps. Results display the main associations and clusters of terms used when assessing the experiential activity, using qualitative techniques. In conclusion, the strengths of data visualization enabling a better understanding of data for qualitative studies are established. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


2013 ◽  
Vol 756-759 ◽  
pp. 1600-1604
Author(s):  
Bin Shi ◽  
Liu Chao Zhao

This paper discusses some basic problems of information visualization and knowledge organization, introducing the basic ideas and applications, and focused on information visualization tools in the field of knowledge organization with examples.


2018 ◽  
Vol 4 ◽  
pp. e25742 ◽  
Author(s):  
Lilliana Sancho-Chavarria ◽  
Fabian Beck ◽  
Daniel Weiskopf ◽  
Erick Mata-Montero

Maintenance and curation of large-sized biological taxonomies are complex and laborious activities. Information visualization systems use interactive visual interfaces to facilitate analytical reasoning on complex information. Several approaches such as treemaps, indented lists, cone trees, radial trees, and many others have been used to visualize and analyze a single taxonomy. In addition, methods such as edge drawing, animation, and matrix representations have been used for comparing trees. Visualizing similarities and differences between two or more large taxonomies is harder than the visualization of a single taxonomy. On one hand, less space is available on the screen to display each tree; on the other hand, differences should be highlighted. The comparison of two alternative taxonomies and the analysis of a taxonomy as it evolves over time provide fundamental information to taxonomists and global initiatives that promote standardization and integration of taxonomic databases to better document biodiversity and support its conservation. In this work we assess how ten user visualization tasks for the curation of biological taxonomies are supported by several visualization tools. Tasks include the identification of conditions such as congruent taxa, splits, merges, and new species added to a taxonomy. We consider tools that have gone beyond the prototype stage, that have been described in peer-reviewed publications, or are in current use. We conclude with the identification of challenges for future development of taxonomy comparison tools.


Author(s):  
Gürdal Ertek ◽  
Mete Sevinç ◽  
Firdevs Ulus ◽  
Özlem Köse ◽  
Güvenç Şahin

The authors present a benchmarking study on the companies in the Turkish food industry based on their financial data. The aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied. Finally, other relevant data, apart from the financial data, is introduced to the analysis through information visualization to discover new insights into DEA results. The results show that the framework developed is a comprehensive and effective strategy for benchmarking; it can be applied in other industries as well. The study contributes to the literature with a novel methodology that integrates the various benchmarking methods from the fields of operations research, machine learning, and financial analysis.


2011 ◽  
Vol 10 (4) ◽  
pp. 289-309 ◽  
Author(s):  
Michael Gleicher ◽  
Danielle Albers ◽  
Rick Walker ◽  
Ilir Jusufi ◽  
Charles D. Hansen ◽  
...  

Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a general understanding of comparative visualization and facilitating the development of more comparative visualization tools.


2002 ◽  
Vol 1 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Ben Shneiderman

The growing use of information visualization tools and data mining algorithms stems from two separate lines of research. Information visualization researchers believe in the importance of giving users an overview and insight into the data distributions, while data mining researchers believe that statistical algorithms and machine learning can be relied on to find the interesting patterns. This paper discusses two issues that influence design of discovery tools: statistical algorithms vs visual data presentation, and hypothesis testing vs exploratory data analysis. The paper claims that a combined approach could lead to novel discovery tools that preserve user control, enable more effective exploration, and promote responsibility.


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