Cited By
View all- Peña-Araya VXue TPietriga EAmsaleg LBezerianos A(2021)HyperStorylines: Interactively untangling dynamic hypergraphsInformation Visualization10.1177/1473871621104500721:1(38-62)Online publication date: 18-Sep-2021
An application of optimization method for storyline based on cluster analysis
Pages 24 - 28
Abstract
As a new visualization technology1, storyline intuitively illustrates the dynamic relationships between entities in a story, which is useful in many applications, including the description of characters' interactions in movies, the evolution of community structure in dynamic social networks, the marital status between people, etc. Previous works optimize the storyline's layout from the perspective of aesthetic standard, significantly reducing line crossings, line wiggles and layout space. But when dealing with large-scale data, there is room for improvement with regard to three issues: insufficient memory space, large time consumption and weak data expression. Therefore, this paper introduces the idea of cluster analysis to the storyline to present the clustering information and reduce the time complexity under the condition where a large number of entities interact in the same period. Meantime a scalable, reusable visualization library of storyline is implemented including some novel interactions.
References
[1]
R. Munroe. Xkcd #657: Movie narrative charts. http://xkcd.com/ 657, December 2009.
[2]
Reda, K., Tantipathananandh, C., Johnson, A., Leigh, J., & Berger-Wolf, T. (2011). Visualizing the evolution of community structures in dynamic social networks. Computer Graphics Forum, 30(3), 1061--1070.
[3]
Kim, N. W., Card, S. K., & Heer, J. (2010). Tracing genealogical data with TimeNets. International Conference on Advanced Visual Interfaces(pp.241--248). ACM.
[4]
Cui, S. Liu, L. Tan, C. Shi, Y. Song, Z. Gao, H. Qu, and X. Tong. (2011). Textflow: Towards better understanding of evolving topics in text. IEEE Transactions on Visualization & Computer Graphics, 17(12), 2412--21.
[5]
V. Ogievetsky. (2009, Mar.) PlotWeaver xkcd/657 creation tool. {Online}. Available: http://ogievetsky.com/PlotWeaver/.
[6]
Ogawa, M., & Ma, K. L. (2010). Software evolution storylines. ACM 2010 Symposium on Software Visualization, Salt Lake City, Ut, Usa, October(pp.35--42). DBLP.
[7]
Tanahashi, Y., & Ma, K. L. (2012). Design considerations for optimizing storyline visualizations. IEEE Transactions on Visualization & Computer Graphics, 18(12), 2679--88.
[8]
Liu, S., Wu, Y., Wei, E., Liu, M., & Liu, Y. (2013). Storyflow: tracking the evolution of stories. IEEE Transactions on Visualization & Computer Graphics, 19(12), 2436--2445.
[9]
Tanahashi, Y., Hsueh, C. H., & Ma, K. L. (2015). An efficient framework for generating storyline visualizations from streaming data. IEEE Transactions on Visualization & Computer Graphics, 21(6), 730--42.
[10]
Borg, I., & Groenen, P. J. F. (1997). Modern Multidimensional Scaling.Modern multidimensional scaling :. Springer.
[11]
Peng, D., Lu, N., Chen, W., & Peng, Q. (2012). Sideknot: revealing relation patterns for graph visualization. 65--72.
[12]
Gansner, E. R., Koutsofios, E., North, S. C., & Vo, K. P. (1993). A technique for drawing directed graphs. Software Engineering IEEE Transactions on, 19(3), 214--230.
[13]
T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 3rd ed. Cambridge, MA, USA: MIT Press, 2009.
Index Terms
- An application of optimization method for storyline based on cluster analysis
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Information & Contributors
Information
Published In
August 2017
158 pages
ISBN:9781450352925
DOI:10.1145/3105971
- General Chair:
- Robert P. Biuk-Aghai,
- Program Chairs:
- Jie Li,
- Shigeo Takahashi
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Sponsors
- KMUTT: King Mongkut's University of Technology Thonburi
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 August 2017
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Author Tags
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- Short-paper
Funding Sources
- Specialized Research Fund for the Doctoral Program of Higher Education
- Natural Science Foundation of China
Conference
VINCI '17
Sponsor:
- KMUTT
VINCI '17: 10th International Symposium on Visual Information Communication and Interaction
August 14 - 16, 2017
Bangkok, Thailand
Acceptance Rates
VINCI '17 Paper Acceptance Rate 12 of 27 submissions, 44%;
Overall Acceptance Rate 71 of 193 submissions, 37%
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View all- Peña-Araya VXue TPietriga EAmsaleg LBezerianos A(2021)HyperStorylines: Interactively untangling dynamic hypergraphsInformation Visualization10.1177/1473871621104500721:1(38-62)Online publication date: 18-Sep-2021
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