臺灣博碩士論文加值系統

多視圖可視化系統 (MV) 是將二至多個視圖組合於單個畫面中，以幫助使用者同時 做出各種決定，這樣的視覺化系統在現今很盛行。由於其廣為人知，研究員們開發了 工具來減輕設計多視圖視覺化的負擔，包括設計視圖的組成以及探索資料事件。然而， 很少有研究開發幫助多視圖可視化顏色設計的工具。儘管顏色可以提供線索並增進可 讀性，但在誤用時也可能使讀者感到困惑。隨著多試圖視覺化的複雜性增加，維持顏 色編碼的一致性變得較困難，因為區分不同數據的顏色數量的需求也在增加，但人類 的短期記憶是有限的。因此，我們提出了一個顏色編碼推薦系統——MVcolor，它對整 個多視圖視覺化使用統一的配色方案，並幫助在有限的顏色數量下維持顏色編碼的一 致性，以及保持足夠的顏色可辨別性。如果待著色不同含義的視覺物件很多，但顏色 資源不足下，那顏色的重複使用必然會發生於多視圖是覺化中的一些不同含義的視覺 物件上。因此，為了可接受的顏色重覆使用，我們的系統根據視覺和語義的相似性， 將那些在多視圖視覺化下的視圖以及待著色的視覺物件分組，並將給定顏色的子集分 配給一組視圖中的視覺物件。我們分別從多視圖視覺化讀者和創作者的角度進行了調 查研究和受控使用者研究，以驗證我們的方法。這兩項使用者研究都驗證了 MVcolor 的有效性。

A multi-view visualization (MV) system that combines two or more views in a single display to help users make various decisions simultaneously has become prevailing nowadays. Due to its prevalence, researchers have developed tools to ease the burden of MV design, including designing compositions and exploring data events. However, few studies developed tools to support the color design for MVs. Although color can provide cues and improve readability, it may confuse readers when misusing it. As the complexity of a MV increases, it would be hard to keep color encodings consistent since the need for the number of colors to distinguish different data also increases, yet human short-term memory is limited. Hence, we propose a color encoding recommendation system, MVcolor, that uses a unified color scheme for the en- tire MV to assist in maintaining color encoding consistency under a limited number of colors and retaining adequate color discriminability. If there are many to-be-colored visual objects with different meanings yet insufficient color resources, color reuse must happen in some visual objects with different meanings in the MV. Therefore, for acceptable color reuse, our system groups the views and those to-be-colored visual objects in a MV based on visual and semantic similarity and assigns a subset of given colors to the visual objects in a group of views. We conducted a survey study and a controlled user study with interviews from MV readers’and creators’perspectives to validate our method, respectively. Both studies verify the effectiveness of MVcolor.

摘要 ....................................................... i
Abstract .................................................. ii
Acknowledgement .......................................... iii
Table of Contents ......................................... iv
List of Figures ........................................... vi
1 Introduction ............................................. 1
2 Related Work ............................................. 5
2.1 Colormap Design Guidelines And Tools ................... 5
2.2 Colors in Multi-View Visualization ..................... 6
2.3 Visualization Recommendation Tools ..................... 7
3 Definition and Design Overview ........................... 9
3.1 Color-Concept Association and Concept Set .............. 9
3.2 Design Goals .......................................... 10
4 Method .................................................. 12
4.1 Concept Set Extraction ................................ 12
4.2 View Grouping and Concept Grouping..................... 13
4.3 Color Assignment ...................................... 16
4.4 Colormap Generation ................................... 19
4.5 Interface and Interaction of MVcolor .................. 19
5 Use Cases ............................................... 22
6 Evaluation .............................................. 25
6.1 User Study - Reader ................................... 25
6.1.1 Study Setup ......................................... 25
6.1.2 Results ............................................. 26
6.2 User Study - Creator .................................. 28
6.2.1 Study Setup ......................................... 28
6.2.2 Results ............................................. 29
7 Discussion .............................................. 33
7.1 Limitation and Future Work ............................ 34
8 Conclusion .............................................. 36
References ................................................ 37
Appendix A More Use Cases ................................. 44
Appendix B Insights from Participants’ Colorings in Creator User Study ...... 47
Appendix C Questionnaire and Interview Questions .......... 49

[1] Z. Qu and J. Hullman, “Keeping multiple views consistent: Constraints, validations, and exceptions in visualization authoring,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, pp. 468–477, 2018.
[2] J. C. Roberts, “Stateoftheart: Coordinated & multiple views in exploratory visualization,” in Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007), 2007, pp. 61–71.
[3] W. Javed and N. Elmqvist, “Exploring the design space of composite visualization,” in 2012 IEEE Pacific Visualization Symposium, 2012, pp. 1–8.
[4] W. Garner and G. L. Felfoldy, “Integrality of stimulus dimensions in various types of information processing,” Cognitive Psychology, vol. 1, no. 3, pp. 225–241, 1970.
[5] P. Bera, “How colors in business dashboards affect users’ decision making,” Commun. ACM, vol. 59, no. 4, p. 50–57, Mar. 2016.
[6] Z. Qu and J. Hullman, “Evaluating visualization sets: Trade-offs between local effectiveness and global consistency,” in Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization, ser. BELIV ’16. Association for Computing Machinery, 2016, p. 44–52.
[7] S. Few, Information Dashboard Design: The Effective Visual Communication of Data. O’Reilly Media, Inc., 2006.
[8] L.Song, N.Peng, C.Xue, and H.Wang, “Color visual cues evaluation method for decision-oriented multiple coordinated views,” in 2019 IEEE Conference on Big Data and Analytics (ICBDA), 2019, pp. 1–6.
[9] M. Q. Wang Baldonado, A. Woodruff, and A. Kuchinsky, “Guidelines for using multiple views in information visualization,” in Proceedings of the Working Conference on Advanced Visual Interfaces, ser. AVI ’00. Association for Computing Machinery, 2000, p. 110–119.
[10] “Vega-lite.” [Online]. Available: https://vega.github.io/vega-lite/
[11] Y. Wang, Z. Sun, H. Zhang, W. Cui, K. Xu, X. Ma, and D. Zhang, “Datashot: Automatic generation of fact sheets from tabular data,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 895–905, 2020.
[12] X. Chen, W. Zeng, Y. Lin, H. M. AI-maneea, J. Roberts, and R. Chang, “Composition and configuration patterns in multiple-view visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1514–1524, 2021.
[13] J. Zhao, S. Xu, S. Chandrasegaran, C. J. Bryan, F. Du, A. Mishra, X. Qian, Y. Li, and K. L. Ma, “Chartstory: Automated partitioning, layout, and captioning of charts into comic-style narratives,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2021.
[14] Y. S. Kristiansen, L. Garrison, and S. Bruckner, “Semantic snapping for guided multi-view visualization design,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, pp. 43–53, 2022.
[15] Y. Zhu, “Measuring effective data visualization,” in Advances in Visual Computing, G. Bebis, R. Boyle, B. Parvin, D. Koracin, N. Paragios, S. M. Tanveer, T. Ju, Z. Liu, S. Coquillart, C. Cruz-Neira, T. Müller, and T. Malzbender, Eds. Springer Berlin Heidelberg, 2007, pp. 652–661.
[16] W. Huang, P. Eades, and S.-H. Hong, “Measuring effectiveness of graph visualizations: A cognitive load perspective,” Information Visualization, vol. 8, no. 3, p. 139–152, 2009.
[17] L. Zhou and C. D. Hansen, “A survey of colormaps in visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 8, pp. 2051–2069, 2016.
[18] M. Stone, D. Albers Szafir, and V. Setlur, “An engineering model for color difference as a function of size,” Color and Imaging Conference, vol. 2014, 11 2014.
[19] D. A. Szafir, “Modeling color difference for visualization design,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 392–401, 2018.
[20] J. Heer and M. Stone, “Color naming models for color selection, image editing and palette design,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ser. CHI ’12. Association for Computing Machinery, 2012, p. 1007–1016.
[21] Q. Zeng, Y. Wang, J. Zhang, W. Zhang, C. Tu, I. Viola, and Y. Wang, “Data-driven colormap optimization for 2d scalar field visualization,” in 2019 IEEE Visualization Conference (VIS), 2019, pp. 266–270.
[22] Y. Wang, X. Chen, T. Ge, C. Bao, M. Sedlmair, C.-W. Fu, O. Deussen, and B. Chen, “Optimizing color assignment for perception of class separability in multiclass scatterplots,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 820–829, 2019.
[23] K. B. Schloss and S. E. Palmer, “Aesthetic response to color combinations: preference, harmony, and similarity,” Attention, Perception & Psychophysics, vol. 73, pp. 551 – 571, 2011.
[24] B. Yang, T. Wei, X. Fang, Z. Deng, F. Li, Y. Ling, and X. Wang, “A color-pair based approach for accurate color harmony estimation,” Computer Graphics Forum, vol. 38, 2019.
[25] P. O’Donovan, A. Agarwala, and A. Hertzmann, “Color compatibility from large datasets,” ACM Trans. Graph., vol. 30, no. 4, 2011.
[26] L. Bartram, A. Patra, and M. Stone, “Affective color in visualization,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ser. CHI ’17. Association for Computing Machinery, 2017, p. 1364–1374.
[27] S. Lin, J. Fortuna, C. Kulkarni, M. Stone, and J. Heer, “Selecting semantically-resonant colors for data visualization,” Computer Graphics Forum (Proc. EuroVis), 2013.
[28] V. Setlur and M. C. Stone, “A linguistic approach to categorical color assignment for data visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 698–707, 2016.
[29] M. Harrower and C. A. Brewer, “Colorbrewer.org: An online tool for selecting colour schemes for maps,” The Cartographic Journal, vol. 40, no. 1, pp. 27–37, 2003.
[30] C. C. Gramazio, D. H. Laidlaw, and K. B. Schloss, “Colorgorical: Creating discriminable and preferable color palettes for information visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 521–530, 2017.
[31] K. Lu, M. Feng, X. Chen, M. Sedlmair, O. Deussen, D. Lischinski, Z. Cheng, and Y. Wang, “Palettailor: Discriminable colorization for categorical data,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 475–484, 2021.
[32] S. Smart, K. Wu, and D. Albers Szafir, “Color crafting: Automating the construction of designer quality color ramps,” IEEE transactions on visualization and computer graphics, vol. PP, 08 2019.
[33] L. Yuan, Z. Zhou, J. Zhao, Y. Guo, F. Du, and H. Qu, “Infocolorizer: Interactive recommendation of color palettes for infographics,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2021.
[34] D. Flatla and C. Gutwin, “Ssmrecolor: Improving recoloring tools with situation-specific models of color differentiation,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ser. CHI ’12. Association for Computing Machinery, 2012, p. 2297–2306.
[35] J. Harper and M. Agrawala, “Deconstructing and restyling d3 visualizations,” in Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology, ser. UIST ’14. Association for Computing Machinery, 2014, p. 253–262.
[36] J. Poco, A. Mayhua, and J. Heer, “Extracting and retargeting color mappings from bitmap images of visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 637–646, 2018.
[37] S. M. Kosslyn, “Understanding charts and graphs: A project in applied cognitive science.” 1983.
[38] S. Zhu, G. Sun, Q. Jiang, M. Zha, and R. Liang, “A survey on automatic infographics and visualization recommendations,” Visual Informatics, vol. 4, no. 3, pp. 24–40, 2020.
[39] J. Mackinlay, “Automatingthedesignofgraphicalpresentationsofrelationalinformation,” ACM Trans. Graph., vol. 5, no. 2, p. 110–141, apr 1986.
[40] J. Mackinlay, P. Hanrahan, and C. Stolte, “Show me: Automatic presentation for visual analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1137–1144, 2007.
[41] K. Wongsuphasawat, D. Moritz, A. Anand, J. Mackinlay, B. Howe, and J. Heer, “Voyager: Exploratory analysis via faceted browsing of visualization recommendations,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 649–658, 2016.
[42] K. Wongsuphasawat, Z. Qu, D. Moritz, R. Chang, F. Ouk, A. Anand, J. Mackinlay, B. Howe, and J. Heer, “Voyager 2: Augmenting visual analysis with partial view specifications,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ser. CHI ’17. Association for Computing Machinery, 2017, p. 2648–2659.
[43] D. Shi, X. Xu, F. Sun, Y. Shi, and N. Cao, “Calliope: Automatic visual data story generation from a spreadsheet,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 453–463, 2021.
[44] K. Hu, M. A. Bakker, S. Li, T. Kraska, and C. Hidalgo, VizML: A Machine Learning Approach to Visualization Recommendation. Association for Computing Machinery, 2019, p. 1–12.
[45] W. Cui, X. Zhang, Y. Wang, H. Huang, B. Chen, L. Fang, H. Zhang, J.-G. Lou, and D. Zhang, “Text-to-viz: Automatic generation of infographics from proportion-related natural language statements,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 906–916, 2020.
[46] X. Qin, Y. Luo, N. Tang, and G. Li, “Deepeye: An automatic big data visualization framework,” Big Data Mining and Analytics, vol. 1, no. 1, pp. 75–82, 2018.
[47] D. Moritz, C. Wang, G. L. Nelson, H. Lin, A. M. Smith, B. Howe, and J. Heer, “Formalizing visualization design knowledge as constraints: Actionable and extensible models in draco,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 438–448, 2019.
[48] K. Schloss, L. Lessard, C. Walmsley, and K. Foley, “Color inference in visual communication: the meaning of colors in recycling,” Cognitive Research: Principles and Implications, vol. 3, 12 2018.
[49] F. Attneave, “Dimensions of similarity,” The American Journal of Psychology, vol. 63, no. 4, pp. 516–556, 1950.
[50] C.Ware,InformationVisualization:PerceptionforDesign,3rded.,ser.MorganKaufmann Series in Interactive Technologies. Morgan Kaufmann, 2012.
[51] “Gurobi optimization.” [Online]. Available: https://www.gurobi.com
[52] G. Sharma, W. Wu, and E. N. Dalal, “The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations,” Color Research & Application, vol. 30, no. 1, pp. 21–30, 2005.
[53] “Deap.” [Online]. Available: https://github.com/deap/deap
[54] “Chroma.js color palette helper.” [Online]. Available: https://gka.github.io/palettes/
[55] “Product-sales-insight-report.” [Online]. Available: https://community.powerbi.com/t5/ Data-Stories-Gallery/Product-Sales-Insight-Report/m-p/1257218
[56] “Employee-attrition-analytics-by-bigintsolutions-com.” [Online]. Avail- able: https://community.powerbi.com/t5/Data-Stories-Gallery/ Employee-Attrition-Analytics-by-BIGINTSolutions-com/td-p/443260
[57] M. Klepsch, F. Schmitz, and T. Seufert, “Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load,” Frontiers in Psychology, vol. 8, p. 1997, 2017.
[58] S. G. Hart and L. E. Staveland, “Development of nasa-tlx (task load index): Results of empirical and theoretical research,” in Human Mental Workload, ser. Advances in Psychology. North-Holland, 1988, vol. 52, pp. 139–183.
[59] J. Brooke, “Sus: A quick and dirty usability scale,” Usability Eval. Ind., vol. 189, 11 1995.
[60] P.Dragicevic,“FairStatisticalCommunicationinHCI,”inModernStatisticalMethodsfor
HCI. Springer, 2016, pp. 291 – 330.
[61] “Incredible-lionel-messi.” [Online]. Available: https://community.powerbi.com/t5/
Data-Stories-Gallery/Incredible-Lionel-Messi/m-p/82225
[62] “Australian-fashion-market-analysis.” [Online]. Available: https://community.powerbi. com/t5/Data-Stories-Gallery/Australian-Fashion-Market-Analysis/m-p/1448397