More Web Proxy on the site http://driver.im/

research-article

An Efficient Framework for Generating Storyline Visualizations from Streaming Data

Authors:

Yuzuru Tanahashi,

Chien-Hsin Hsueh,

Kwan-Liu MaAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 21, Issue 6

Pages 730 - 742

https://doi.org/10.1109/TVCG.2015.2392771

Published: 01 June 2015 Publication History

Abstract

This paper presents a novel framework for applying storyline visualizations to streaming data. The framework includes three components: a new data management scheme for processing and storing the incoming data, a layout construction algorithm specifically designed for incrementally generating storylines from streaming data, and a layout refinement algorithm for improving the legibility of the visualization. By dividing the layout computation to two separate components, one for constructing and another for refining, our framework effectively provides the users with the ability to follow and reason dynamic data. The evaluation studies of our storyline visualization framework demonstrate its efficacy to present streaming data as well as its superior performance over existing methods in terms of both computational efficiency and visual clarity.

References

[1]

Y. Tanahashi and K.-L. Ma, “Design considerations for optimizing storyline visualizations,” IEEE Trans. Vis. Comput. Graph., vol. 18, no. 12, pp. 2679 –2688, Dec. 2012.

Digital Library

[2]

S. Liu, Y. Wu, E. Wei, M. Liu, and Y. Liu, “Storyflow: Tracking the evolution of stories,” IEEE Trans. Vis. Comput. Graph., vol. 19, no. 12, pp. 2436–2445, Dec. 2013.

Digital Library

[3]

R. Munroe. (2009). xkcd #657: Movie narrative charts. [Online]. Available: http://xkcd.com/657

[4]

M. J. Kraak, “The space-time cube revisited from a geovisualization perspective, ” in Proc. 21st Int. Cartograph. Conf., 2003, pp. 1988 –1995.

[5]

P. Gatalsky, N. Andrienko, and G. Andrienko, “ Interactive analysis of event data using space-time cube,” in Proc. 8th Int. Conf. Inf. Vis., 2004, pp. 145–152.

[6]

T. Kapler and W. Wright, “Geotime information visualization,” in Proc. IEEE Symp. Inf. Vis. , 2004, pp. 25–32.

Digital Library

[7]

U. Brandes and S. Corman, “Visual unrolling of network evolution and the analysis of dynamic discourse, ” in Proc. IEEE Symp. Inf. Vis., 2002, pp. 145–151.

[8]

G. Kumar and M. Garland, “Visual exploration of complex time-varying graphs,” IEEE Trans. Vis. Comput. Graph., vol. 12, no. 5, pp. 805–812, Sep./Oct. 2006.

Digital Library

[9]

M. Itoh, M. Toyoda, and M. Kitsuregawa, “An interactive visualization framework for time-series of web graphs in a 3D environment,” in Proc. 14th Int. Conf. Inf. Vis., 2010, pp. 54–60.

[10]

P. Kristensson, N. Dahlback, D. Anundi, M. Bjornstad, H. Gillberg, J. Haraldsson, I. Martensson, M. Nordvall, and J. Stahl, “An evaluation of space time cube representation of spatiotemporal patterns,” IEEE Trans. Vis. Comput. Graph., vol. 15, no. 4, pp. 696–702, Jul/Aug. 2009.

Digital Library

[11]

T. Crnovrsanin, C. Muelder, C. Correa, and K.-L. Ma, “Proximity-based visualization of movement trace data,” in Proc. IEEE Symp. Vis. Anal. Sci. Technol., Oct. 2009, pp. 11–18.

[12]

A. Shrestha, B. Miller, Y. Zhu, and Y. Zhao, “Storygraph: Extracting patterns from spatio-temporal data,” in Proc. ACM SIGKDD Workshop Interact. Data Exploration Anal. , 2013, pp. 95–103.

[13]

T. Falkowski, B. Bartelheimer, and M. Spiliopoulou, “ Mining and visualizing the evolution of subgroups in social networks,” in Proc. IEEE/WIC/ACM Int. Conf. Web Intell., 2006, pp. 52–58.

[14]

A. Bezerianos, P. Dragicevic, J. Fekete, J. Bae, and B. Watson, “Geneaquilts: A system for exploring large genealogies,” IEEE Trans. Vis. Comput. Graph., vol. 16, no. 6, pp. 1073–1081, Nov/Dec. 2010.

Digital Library

[15]

K. Reda, C. Tantipathananandh, A. Johnson, J. Leigh, and T. Berger-Wolf, “Visualizing the evolution of community structures in dynamic social networks,” in Proc. 13th Eur./IEEE-VGTC Conf. Vis., 2011, pp. 1061–1070.

[16]

N. W. Kim, S. K. Card, and J. Heer, “Tracing genealogical data with timenets,” in Proc. Int. Conf. Adv. Vis. Interfaces, 2010, pp. 241–248.

[17]

W. Cui, S. Liu, L. Tan, C. Shi, Y. Song, Z. Gao, H. Qu, and X. Tong, “Textflow: Towards better understanding of evolving topics in text,” IEEE Trans. Vis. Comput. Graph., vol. 17, no. 12, pp. 2412–2421, Dec. 2011.

Digital Library

[18]

V. Ogievetsky. (2009, Mar.) PlotWeaver xkcd/657 creation tool. [Online]. Available: http://ogievetsky.com/PlotWeaver/

[19]

M. Ogawa and K.-L. Ma, “Software evolution storylines,” in Proc. 5th Int. Symp. Softw. Vis., 2010, pp. 35–42.

Digital Library

[20]

C. Muelder, T. Crnovrsanin, A. Sallaberry, and K.-L. Ma, “Egocentric storylines for visual analysis of large dynamic graphs,” in Proc. IEEE Int. Conf. Big Data, 2013, pp. 56–62.

[21]

S. Boettcher and A. G. Percus, “Extremal optimization: Methods derived from co-evolution,” in Proc. Genetic Evol. Comput. Conf., 1999, vol. 1, pp. 825– 832.

[22]

M. L. Huang, P. Eades, and J. Wang, “On-line animated visualization of huge graphs using a modified spring algorithm,” J. Visual Lang. Comput., vol. 9, no. 6, pp. 623–645, 1998.

Digital Library

[23]

H. Purchase, E. Hoggan, and C. Grg, “How important is the mental map? An empirical investigation of a dynamic graph layout algorithm,” in Graph Drawing, New York, NY, USA: Springer, 2007, vol. 4372, pp. 184–195.

[24]

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 3rd ed. Cambridge, MA, USA: MIT Press, 2009.

Digital Library

[25]

Y. Tanahashi. (2013, Nov.) Movie interaction datasets [Online]. Available: http://vis.cs.ucdavis.edu/~t̃anahashi/data_downloads/storyline_vi sualizations/.

[26]

M. Andersen and L. Vandenberghe. Cvxopt: Python software for convex optimization [Online]. Available: http://cvxopt.org/, Mar. 2014.

[27]

E. Gansner, E. Koutsofios, S. North, and K.-P. Vo, “A technique for drawing directed graphs,” IEEE Trans. Softw. Eng., vol. 19, no. 3, pp. 214–230, Mar. 1993.

Digital Library

[28]

K. Sugiyama, S. Tagawa, and M. Toda, “Methods for visual understanding of hierarchical system structures,” IEEE Trans. Syst., Man Cybern , vol. 11, no. 2, pp. 109–125, Feb. 1981.

[29]

Z. Maoz. (2005). Dyadic MID Dataset (version 2.0) [Online]. Available: http://vanity.dss.ucdavis.edu/~maoz/dyadmid.html

[30]

M. Ogawa. Software development history logs [Online]. Available: http://vis.cs.ucdavis.edu/~ogawa/datasets/, Jan. 2015.

[31]

W. W. Cohen. Enron email dataset [Online]. Available: https://www.cs.cmu.edu/~enron/, Jan. 2015.

[32]

A. Clauset, M. E. J. Newman, and C. Moore, “ Finding community structure in very large networks,” Phy. Rev. E, vol. 70, pp. 1–6, 2004.

[33]

M. ApS. Mosek package [Online]. Available: http://www.mosek.com, Mar. 2014.

[34]

International Business Machines Corp. CPLEX Optimizer [Online]. Available: http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/, Mar. 2014.

[35]

T. Mitra and E. Gilbert, “Have you heard?: How gossip flows through workplace email,” in Proc. AAAI Int. Conf. Weblogs Soc. Media, 2012, pp. 242–249.

[36]

S. Havre, B. Hetzler, and L. Nowell, “Themeriver: Visualizing theme changes over time,” in Proc. IEEE Symp. Inform. Vis., 2000, pp. 115–123.

[37]

L. Byron and M. Wattenberg, “Stacked graphs-geometry & aesthetics,” IEEE Trans. Vis. Comput. Graph., vol. 14, no. 6, pp. 1245–1252, Nov. 2008.

Digital Library

[38]

M. Rosvall and C. T. Bergstrom, “Mapping change in large networks,” PLoS ONE , vol. 5, no. 1, p. e8694, 2010.

[39]

T. Chen, A. Lu, and S.-M. Hu, “Visual storylines: Semantic visualization of movie sequence,” Comput. Graph., vol. 36, no. 4, pp. 241–249, 2012.

Digital Library

Cited By

Kuo YLiu DMa K(2024)SpreadLine: Visualizing Egocentric Dynamic InfluenceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345637331:1(1050-1060)Online publication date: 13-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456373
Deng ZChen SSchreck TDeng DTang TXu MWeng DWu Y(2024)Visualizing Large-Scale Spatial Time Series with GeoChronIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332716230:1(1194-1204)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327162
House NJohnston A(2023)Clover Connections: Visualising Character Dynamics in Novels for Non-ExpertsProceedings of the 35th Australian Computer-Human Interaction Conference10.1145/3638380.3638384(191-201)Online publication date: 2-Dec-2023
https://dl.acm.org/doi/10.1145/3638380.3638384
Show More Cited By

Index Terms

An Efficient Framework for Generating Storyline Visualizations from Streaming Data
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

Recommendations

Yarn: Generating Storyline Visualizations Using HTN Planning
GI '18: Proceedings of the 44th Graphics Interface Conference

Existing storyline visualization techniques represent narratives as a node-link graph where a sequence of links shows the evolution of causal and temporal relationships between characters in the narrative. These techniques make a number of simplifying ...
A dynamic balanced quadtree for real-time streaming data
Abstract
With the advent of big data, industries and individuals generate a large amount of stream data through various intelligent devices. Stream data arrive gradually over time, unlike historical data. This causes an increasingly unbalanced ...
Highlights
- Formally defines the quadtree imbalance problem caused by constant stream data density.
Storyline Visualizations with Ubiquitous Actors
Graph Drawing and Network Visualization
Abstract
Storyline visualizations depict the temporal dynamics of social interactions, as they describe how groups of actors (individuals or organizations) change over time. A common constraint in storyline visualizations is that an actor cannot belong to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 21, Issue 6

June 2015

97 pages

ISSN:1077-2626

Issue’s Table of Contents

Copyright © 2015.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 June 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kuo YLiu DMa K(2024)SpreadLine: Visualizing Egocentric Dynamic InfluenceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345637331:1(1050-1060)Online publication date: 13-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456373
Deng ZChen SSchreck TDeng DTang TXu MWeng DWu Y(2024)Visualizing Large-Scale Spatial Time Series with GeoChronIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332716230:1(1194-1204)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327162
House NJohnston A(2023)Clover Connections: Visualising Character Dynamics in Novels for Non-ExpertsProceedings of the 35th Australian Computer-Human Interaction Conference10.1145/3638380.3638384(191-201)Online publication date: 2-Dec-2023
https://dl.acm.org/doi/10.1145/3638380.3638384
Wang YSun GZhu ZLi TChen LLiang R(2023)EStoryline: Visualizing the Relationship with Triplet Entities and Event DiscoveryACM Transactions on Intelligent Systems and Technology10.1145/363351915:1(1-26)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3633519
Wallner GWang LDormann C(2023)Visualizing the Spatio-Temporal Evolution of Gameplay using Storyline Visualization: A Study with League of LegendsProceedings of the ACM on Human-Computer Interaction10.1145/36110587:CHI PLAY(1002-1024)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3611058
Shilpika Fujiwara TSakamoto NNonaka JMa K(2022)A Visual Analytics Approach for Hardware System Monitoring with Streaming Functional Data AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.316534828:6(2338-2349)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1109/TVCG.2022.3165348
Chen XZhang JFu CFekete JWang Y(2022)Pyramid-based Scatterplots Sampling for Progressive and Streaming Data VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311488028:1(593-603)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114880
Tang JZhou YTang TWeng DXie BYu LZhang HWu Y(2022)A Visualization Approach for Monitoring Order Processing in E-Commerce WarehouseIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311487828:1(857-867)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114878
Zhu ZShen YZhu SZhang GLiang RSun G(2022)Towards better pattern enhancement in temporal evolving set visualizationJournal of Visualization10.1007/s12650-022-00896-x26:3(611-629)Online publication date: 27-Nov-2022
https://dl.acm.org/doi/10.1007/s12650-022-00896-x
Valdivia PBuono PPlaisant CDufournaud NFekete J(2021)Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.293319627:1(1-13)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TVCG.2019.2933196
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents