Strategies for evaluating visual analytics systems: A systematic review and new perspectives

Author:

Islam Md Rafiqul1ORCID,Akter Shanjita2,Islam Linta3,Razzak Imran4,Wang Xianzhi5,Xu Guandong1

Affiliation:

1. Data Science Institute, University of Technology Sydney, Broadway, NSW, Australia

2. School of Computer Science, Taylors University, Subang Jaya, Selangor, Malaysia

3. Department of Biological Science, Louisiana State University, Baton Rouge, LA, USA

4. School of Computer Science and Engineering, UNSW, Sydney, NSW, Australia

5. School of Computer Science, University of Technology Sydney, Ultimo, NSW, Australia

Abstract

In recent times, visual analytics systems (VAS) have been used to solve various complex issues in diverse application domains. Nonetheless, an inherent drawback arises from the insufficient evaluation of VAS, resulting in occasional inaccuracies when it comes to analytical reasoning, information synthesis, and deriving insights from vast, ever-changing, ambiguous, and frequently contradictory data. Hence, the significance of implementing an appropriate evaluation methodology cannot be overstated, as it plays a pivotal role in enhancing the design and development of visualization systems. This paper assesses visualization systems by providing a systematic exploration of various evaluation strategies (ES). While several existing studies have examined some ES, the extent of comprehensive and systematic review for visualization research remains limited. In this work, we introduce seven state-of-the-art and widely recognized ES namely (1) dashboard comparison; (2) insight-based evaluation; (3) log data analysis; (4) Likert scales; (5) qualitative and quantitative analysis; (6) Nielsen’s heuristics; and (7) eye trackers. Moreover, it delves into their historical context and explores numerous applications where these ES have been employed, shedding light on the associated evaluation practices. Through our comprehensive review, we overview and analyze the predominant evaluation goals within the visualization community, elucidating their evolution and the inherent contrasts. Additionally, we identify the open challenges that arise with the emergence of new ES, while also highlighting the key themes gleaned from the existing literature that hold potential for further exploration in future studies.

Funder

Australian Research Council

Publisher

SAGE Publications

Subject

Computer Vision and Pattern Recognition

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Designing Interactive Analytics Dashboards for Diverse Target Groups: The Process and Decision-Making;2024 9th International Conference on Smart and Sustainable Technologies (SpliTech);2024-06-25

2. Machine Learning with Explainability for Suicide Ideation Detection from Social Media Data;2023 10th International Conference on Behavioural and Social Computing (BESC);2023-10-30

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