Interactivity in Visual Analytics: Use of Conceptual Frameworks to Support Human-Centered Design of a Decision-Support Tool

Parsons, Paul; Sedig, Kamran; Didandeh, Arman; Khosravi, Arash

doi:10.1109/hicss.2015.138

Cited by 14 publications

(11 citation statements)

References 16 publications

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“…Giereth and Ertl introduced three further design patterns [GE08]. Since then, there have been a few attempts to develop and use design patterns for visualization (e.g., [HLZY10,PSDK15]). One major stumbling block is that there will be a huge list of design patterns that record solutions for different problems, considering the complexity of the design space of VA workflows [TK09, REE * 09].…”

Section: Design and Evaluation Methods For Visualization And Vamentioning

confidence: 99%

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

Chen

Ebert

2019

Computer Graphics Forum

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Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipating secondary effects of the solutions. The methodology is built on the theoretical premise that designing a VA workflow is an optimization of the cost‐benefit ratio of the processes in the workflow. It makes uses three fundamental measures to group and connect “symptoms”, “causes”, “remedies”, and “side‐effects”, and guide the search for potential solutions to the problems. In terms of requirement analysis and system design, the proposed methodology can enable system designers to explore the decision space in a structured manner. In terms of evaluation, the proposed methodology is time‐efficient and complementary to various forms of empirical studies, such as user surveys, controlled experiments, observational studies, focus group discussions, and so on. In general, it reduces the amount of trial‐and‐error in the lifecycle of VA system development.

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Section: Design and Evaluation Methods For Visualization And Vamentioning

confidence: 99%

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

Chen

Ebert

2019

Computer Graphics Forum

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“…Data visualisation techniques can offer abilities for data interpretation to make raw data expressive, providing a practical and utilised way to present data in a meaningful way. Multiple visualisation tools have been adopted to visually exploit data insights in many application domains, including the stock market [6][7][8]. Currently, stock data analytics is still usually being conducted in traditional ways such as a spreadsheet, charts, treemaps and parallel coordinates, etc., with these methods improving the readability of finalised stock data to some extent.…”

Section: Graph Visualisationmentioning

confidence: 99%

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Hua

Huang

2019

Symmetry

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The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.

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“…Many visualisation tools have all been adopted to visually exploit data insights in many application domains [47,48]. However, to the best of our knowledge, few attempts have been made to conduct visual analytics of scientific standard datasets [1,3], although some similar studies in other fields do analyse particular relevant standards.…”

Section: Motivations and Contributionsmentioning

confidence: 99%

Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets

Hua

Huang

et al. 2019

Symmetry

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Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards.

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Interactivity in Visual Analytics: Use of Conceptual Frameworks to Support Human-Centered Design of a Decision-Support Tool

Cited by 14 publications

References 16 publications

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets

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