Skip to main content

A Model for Self-Organizing Data Visualization Using Decentralized Multiagent Systems

  • Chapter
  • First Online:
Book cover Advances in Applied Self-organizing Systems

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Ankerst, M., Berchtold, S., and Keim, D. A. (1998). Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In Proceedings of Symposium on Information Visualization (Infovis’98), pages 52–60, IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Bartram, L., and Ware, C. (2002). Filtering and brushing with motion. Journal of Information Visualization, 1(1): 66–79.

    Article  Google Scholar 

  • Bentley, C. L., and Ward, M. O. (1996). Animating multidimensional scaling to visualize N-dimensional data sets. In Proceedings of Symposium on Information Visualization (Infovis’96), pages 72–73, IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Brodlie, K. W., Brooke, J., Chen, M., Chisnall, D., Hughes, C. J., John, N. W. (2006). A framework for adaptive visualization. IEEE Visualization 2006, Baltimore, MD, IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Card, S. K., Mackinlay, J. D., and Shneiderman, B. (1999). Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, San Francisco.

    Google Scholar 

  • Chen, L., Xu, X., Chen, Y., and He, P. (2004). A novel ant clustering algorithm based on cellular automata. In Proceedings of International Conference of the Intelligent Agent Technology (IAT’04), pages 148–154, IEEE Computer Society, Washington DC, USA.

    Google Scholar 

  • Chi, E. H. (2000). A taxonomy of visualization techniques using the data state reference model. In Proceedings of IEEE Symposium on Information Visualization (Infovis), pages 69–75.

    Google Scholar 

  • Couzin, I. D., Krause, J., James, R., Ruxton, G. D., and Franks, N. R. (2002). Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology, 218:1–11.

    Article  MathSciNet  Google Scholar 

  • Davies, J. (2004). Why birds fly in formation: A new interpretation. Interpretive Birding, 5(2).

    Google Scholar 

  • Decker, K. S., and Sycara, K. (1997). Intelligent adaptive information agents. Journal of Intelligent Information Systems, 9(3):239–260.

    Article  Google Scholar 

  • Deneubourg, J., Goss, S., Franks, N., A., S. F., Detrain, C., and Chretian, L. (1990). The dynamics of collective sorting: Robot-like ants and ant-like robots. From Animals to Animats: 1st International Conference on Simulation of Adaptative Behaviour, pages 356–363.

    Google Scholar 

  • Ebert, A., Bender, M., Barthel, H., and Divivier, A. (2001). Tuning a component-based visualization system architecture by agents. In Proceedings of International Symposium on Smart Graphics, IBM T.J. Watson Research Center.

    Google Scholar 

  • Eick, S. G. (2001). Visualizing online activity. Communications of the ACM, 44(8):45–50.

    Article  Google Scholar 

  • Franklin, S., and Graesser, A. (1996). Is it an agent, or just a program? A taxonomy for autonomous agents. In Proceedings of Third International Workshop on Agent Theories, Architectures, and Languages (ATAL’96), pages 21–35, Springer, Heidelberg.

    Google Scholar 

  • Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game ‘Life.’ Scientific American 223:120–123.

    Article  Google Scholar 

  • Hamilton, W. D. (1971). Geometry for the selfish herd. Journal for Theoretical Biology, 31: 295–311.

    Article  Google Scholar 

  • Handl, J., and Meyer, B. (2002). Improved ant-based clustering and sorting in a document retrieval interface. In Proceedings of International Conference on Parallel Problem Solving from Nature (PPSN VII), volume 2439 of Lecture Notes of Computer Science, pages 913–923, Springer, Heidelberg.

    Google Scholar 

  • Handl, J., Knowles, J., and Dorigo, M. (2005). Ant-based clustering and topographic mapping. Artificial Life, 12(1):35–61.

    Article  Google Scholar 

  • Healey, C. G., Amant, R. S., and Chang, J. (2001). Assisted visualization of E-commerce auction agents. In Proceedings of Graphics Interface 2001, Ottawa, Canada, pages 201–208, Lawrence, Erlbaum Associate, New Jersey.

    Google Scholar 

  • Hellerstein, J. M., Chou, A., Hidber, C., Olston, C., Raman, V., Roth, T., et al. (1999). Interactive data analysis: The control project. IEEE Computer, 32(8):51.

    Article  Google Scholar 

  • Hiraishi, H., Sawai, H., and Mizoguchi, F. (2001). Design of a visualization agent for WWW information. Volume 2112 of Lecture Notes in Computer Science, pages 249–258, Springer, Heidelberg.

    Google Scholar 

  • Ishizaki, S. (1996). Multiagent model of dynamic design: Visualization as an emergent behavior of active design agents. In Proceedings of SIGCHI Conference on Human Factors in Computing Systems (CHI’96), Vancouver, British Columbia, Canada, pages 347–354.

    Google Scholar 

  • Jankun-Kelly, T. J., Ma, K.-L., and Gertz, M. (2002). A model for the visualization exploration process. In Proceedings of IEEE Visualization, Boston, IEEE Computer Society, Washington, DC, USA.

    Google Scholar 

  • Kadrovach, B. A., and Lamont, G. B. (2002). A particle swarm model for swarm-based networked sensor systems. In Proceedings of ACM Symposium on Applied Computing, Madrid, Spain, pages 918–924, ACM, New York.

    Google Scholar 

  • Kennedy, J., and Eberhart, R. C. (2001). Swarm Intelligence. Morgan Kaufmann, San Francisco.

    Google Scholar 

  • Kramer, P., and Yantis, S. (1997). Perceptual grouping in space and time: Evidence from the Ternus display. Perception and Psychophysics, 59(1):87–99.

    Article  Google Scholar 

  • Kuntz, A., Layzell, P., and Snyers, D. (1997). A colony of ant-like agents for partitioning in VLSI technology. In Proceedings of European Conference on Articial Life, pages 417–424. The MIT Prss, Cambridge.

    Google Scholar 

  • Labroche, N., Monmarché, N., and Venturini, G. (2002). A new clustering algorithm based on the chemical recognition system of ants. In Proceedings of European Conference on Artificial Intelligence, Lyon, France, pages 345–349, IOS Press, Amsterdam.

    Google Scholar 

  • Lander, J. (1998). Ocean spray in your face. Game Developer, 9–13.

    Google Scholar 

  • Lethbridge, T. C., and Ware, C. (1990). Animation using behavior functions. In T. Ichikawa, E. Jungert and R. R. Korfhage, editors, Visual Languages and Applications, pages 237-252. Plenum Press, New York.

    Chapter  Google Scholar 

  • Lumer, E. D., and Faieta, B. (1994). Diversity and adaptation in populations of clustering ants. From Animals to Animats: Conference on Simulation of Adaptative Behaviour, pages 501–508, The MIT Press, Cambridge.

    Google Scholar 

  • Macgill, J., and Openshaw, S. (1998,). The use of flocks to drive a geographic analysis machine. In Proceedings of International Conference on GeoComputation, Bristol, United Kingdom. GeoComputation CD-ROM, Manchester.

    Google Scholar 

  • Mackinlay, J. D. (1986). Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5(2): 110–141.

    Article  Google Scholar 

  • Marefat, M. M., Varecka, A. F., and Yost, J. (1997). An intelligent visualization agent for simulation-based decision support. Computing in Science and Engineering, 4(3), 72–82.

    Google Scholar 

  • Martin, A. (1999). Particle systems. retrieved August, 2006, from http://www.cs.wpi.edu/∼matt/courses/cs563/talks/psys.html

    Google Scholar 

  • Mason, K., Denzinger, J., and Carpendale, S. (2004). Negotiating Gestalt: Artistic expression and coalition formation in multiagent systems. In Proceedings of International Symposium on Smart Graphics, pages 103–115.

    Google Scholar 

  • Ogston, E., Overeinder, B., van Steen, M., and Brazier, F. (2003). A method for decentralized clustering in large multi-agent systems. In Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems, Melbourne, Australia, pages 789–796, ACM, New York.

    Google Scholar 

  • Pham, B., and Brown, R. (2003). Multi-agent approach for visualisation of fuzzy systems. Volume 2659 of Lecture Notes in Computer Science, pages 995–1004, Springer, Berlin.

    Google Scholar 

  • Proctor, G., and Winter, C. (1998). Information flocking: Data visualisation in virtual worlds using emergent behaviours. In Proceedings of Virtual Worlds 98, Paris, France, pages 168–176, Springer, Berlin.

    Google Scholar 

  • Ramos, V., and Abraham, A. (2004). Evolving a stigmergic self-organized data-mining. In Proceedings of International Conference on Intelligent Systems, Design and Applications (ISDA-04), Budapest, Hungary, pages 725-730, IEEE Computer Society, Washington, DC, USA.

    Google Scholar 

  • Reeves, W. T. (1983). Particle systems: A technique for modeling a class of fuzzy objects. Computer-Graphics, 17(3):359–376.

    Article  Google Scholar 

  • Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioral model. Computer Graphics, 21(4):25–34.

    Article  Google Scholar 

  • Roard, N., and Jones, M. W. (2006). Agent based visualization and strategies. In Proceedings of Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), Pilzen, Czech Republic. University of West Bohemia, Plzen.

    Google Scholar 

  • Robinson, N., and Shapcott, M. (2002). Data mining information visualisation: Beyond charts and graphs. In Proceedings of International Conference on Information Visualisation, London, pages 577–583.

    Google Scholar 

  • Sadok, B. Y., and Engelbert, M. N. (2004). Emulating a cooperative behavior in a generic association rule visualization tool. In Proceedings of IEEE International Conference on Tools with Artificial Intelligence (ICTAI’04), Washington, DC, USA, pages 148–155, IEEE Computer Society, Washington, DC, USA.

    Google Scholar 

  • Schroeder, M., and Noy, P. (2001). Multi-agent visualisation based on multivariate data. In Proceedings of International Conference on Autonomous Agents, Montreal, Quebec, Canada, pages 85–91, ACM, New York.

    Google Scholar 

  • Senay, H., and Ignatius, E. (1994). A knowledge-based system for visualization design. IEEE Computer Graphics and Applications, 14(6):36–47.

    Article  Google Scholar 

  • Shneiderman, B. (1998). Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley.

    Google Scholar 

  • Stanton, A., and Unkrich, L. (Writers) (2003). Finding Nemo. In W. D. P. P. A. Studios (Producer), USA. Buena Vista Pictures / Walt Disney Pictures.

    Google Scholar 

  • SWCP. (2003). Historical Data for S&P 500 Stocks. Retrieved October, 2006, from http://kumo.swcp.com/stocks

    Google Scholar 

  • Tonnesen, D. (2001). Particle systems for artistic expression. In Proceedings of Subtle Technologies Conference, Toronto, Canada, pages 17–20, University of Toronto, Toronto.

    Google Scholar 

  • Torgerson, W. S. (1952). Multidimensional scaling. Psychometrika, 17:401–419.

    Article  MathSciNet  Google Scholar 

  • Tory, M., and Möller, T. (2004). Rethinking visualization: A high-level taxonomy. In Proceedings of IEEE Symposium on Information Visualization (Infovis’04), Austin, Texas, pages 151–158.

    Google Scholar 

  • Tufte, E. R. (2001). The Visual Display of Quantitative Information. Graphics Press, Cheshire.

    Google Scholar 

  • Upson, C., Faulhaber, T. A., Jr., Kamins, D., Laidlaw, D., Schlegel, D., Vroom, J. (1989). The application visualization system: a computational environment for scientific visualization. IEEE Computer Graphics and Applications, 9:30–42.

    Article  Google Scholar 

  • van der Burg, J. (2000). Building an advanced particle system. Game Developer, 44–50.

    Google Scholar 

  • Vande Moere, A. (2004). Time-varying data visualization using information flocking boids. In Proceedings of Symposium on Information Visualization (Infovis’04), Austin, USA, pages 97–104.

    Google Scholar 

  • Vande Moere, A., and Clayden, J. J. (2005). Cellular ants: Combining ant-based clustering with cellular automata. In Proceedings of IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05), pages 177-184.

    Google Scholar 

  • Vande Moere, A., Mieusset, K. H., and Gross, M. (2004). Visualizing abstract information using motion properties of data-driven infoticles. In Proceedings of Conference on Visualization and Data Analysis 2004 (IS&T/SPIE Symposium on Electronic Imaging), pages 33–44. San Jose, CA.

    Google Scholar 

  • Vande Moere, A., Clayden, J. J., and Dong, A. (2006). Data clustering and visualization using cellular automata ants. In Proceedings of ACS Australian Joint Conference on Artificial Intelligence (AI’06), Hobart, Australia, pages 826–836, Springer, Berlin.

    Google Scholar 

  • Von Neumann, J. (1966). Theory of Self-Reproducing Automata. University of Illinois Press, Chicago.

    Google Scholar 

  • Ware, C. (2000). Information Visualization Perception for Design. Morgan Kaufmann, San Francisco.

    Google Scholar 

  • Ware, C., Neufeld, E., and Bartram, L. (1999). Visualizing causal relations. In Proceedings of IEEE Symposium on Information Visualization (Infovis’99), San Francisco, CA, pages 39–42, IEEE Computer Society, Washington, DC, USA.

    Google Scholar 

  • Wojciech, B. (2001). Multivariate visualization techniques. Retrieved June 2006, from http://www.pavis.org/essay/multivariate_visualization_techniques.html

    Google Scholar 

  • Woolridge, M. (2001). Introduction to Multiagent Systems. Wiley, New York.

    Google Scholar 

  • Zambonelli, F., Mamei, M., and Roli, A. (2002). What can cellular automata tell us about the behavior of large multi-agent systems? In A. Omicini and J. Castro, editors, volume 2603 of Lecture Notes in Computer Science, pages 216–231. Springer, Berlin.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag London Limited

About this chapter

Cite this chapter

Moere, A.V. (2008). A Model for Self-Organizing Data Visualization Using Decentralized Multiagent Systems. In: Prokopenko, M. (eds) Advances in Applied Self-organizing Systems. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-982-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-84628-982-8_13

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-981-1

  • Online ISBN: 978-1-84628-982-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics