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Evaluation on visualization methods of dynamic collaborative relationships for project management

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Abstract

Using visualization methods to describe collaborative relationships can form a more intuitive and conducive graphical representation of these relationships, helping us better understand and analyze complex dynamic collaborative relationships. To explore a suitable visualization form for collaborative relationship analysis, we propose a task classification method to evaluate the two visual methods (node-link and adjacency matrix) which represent the static features and the three methods (animation, small multiples, and timeline) which represent the time characteristics of dynamic graphs. We present an evaluation system and design a task-based user evaluation experiment with the Dutch railway project data. By collecting and analyzing task completion time and error rates, we summarize our findings from the evaluation experiment and list three key recommendations to provide preliminary clues to visual designers: (1) Node-link has a better performance on small-scale project management. (2) Timeline has more advantages in the expression of project time management. (3) Animation will be a good choice when you need to check the status of tasks in the project management for a period of time. These findings can help the designers discover faster and more accurate ways to visualize the characteristics and changes of collaborative relationships, thus promoting the smooth progress of collaborative work.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61972130 and 61906061, partly supported by the Key Research and Development Plan of Anhui Province under Grant No. 1704d0802177, partly supported by the Natural Science Foundation of Anhui Province of China under Grant No. 1708085MF158, and also partly supported by the Key Project of Transformation and Industrialization of Scientific and Technological Achievements of Intelligent Manufacturing Technology Research Institute of Hefei University of Technology under Grant No. IMICZ2017010.

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, Hefei, China

    Qiang Lu, Jing Huang, Qingyu Zhang & Jie Li

  2. School of Computer Science and Information, Hefei University of Technology, Hefei, 230601, China

    Qiang Lu

  3. University of North Texas, Denton, TX, USA

    Xiaohui Yuan

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  1. Qiang Lu
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  3. Qingyu Zhang
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  4. Xiaohui Yuan
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  5. Jie Li
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Correspondence to Qiang Lu.

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Lu, Q., Huang, J., Zhang, Q. et al. Evaluation on visualization methods of dynamic collaborative relationships for project management. Vis Comput 37, 161–174 (2021). https://doi.org/10.1007/s00371-019-01789-1

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