International Journal of Computer Games Technology
Volume 2008 (2008), Article ID 906931, 9 pages
doi:10.1155/2008/906931
Research Article

Visualization of Online-Game Players Based on Their Action Behaviors

Ruck Thawonmas1 and Keita Iizuka1,2

1Intelligent Computer Entertainment Laboratory, Graduate School of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
2Solution Development Team, Solution Development Department, Bandai Networks Co., Ltd., Tokyo 111-8081, Japan

Received 1 February 2008; Revised 8 May 2008; Accepted 11 June 2008

Academic Editor: Jouni Smed

Copyright © 2008 Ruck Thawonmas and Keita Iizuka. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Harding-Rolls, “Western World MMOG Market: 2006 review and forecasts to 2011,” Screendigest Management Report, Screen Digest, London, UK, March 2007.
  2. K. Börner and S. Penumarthy, “Social diffusion patterns in three-dimensional virtual worlds,” Information Visualization, vol. 2, no. 3, pp. 182–198, 2003.
  3. N. Hoobler, G. Humphreys, and M. Agrawala, “Visualizing competitive behaviors in multi-user virtual environments,” in Proceedings of the 15th IEEE Visualization Conference (VIS '04), pp. 163–170, Austin, Tex, USA, October 2004.
  4. L. Chittaro, R. Ranon, and L. Leronutti, “VU-Flow: a visualization tool for analyzing navigation in virtual environments,” IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 6, pp. 1475–1485, 2006.
  5. R. Thawonmas, M. Hirano, and M. Kurashige, “Cellular automata and Hilditch thinning for extraction of user paths in online games,” in Proceedings of 5th ACM SIGCOMM Workshop on Network & System Support for Games (NetGames '06), Singapore, October 2006.
  6. R. Thawonmas, M. Kurashige, and K. T. Chen, “Detection of landmarks for clustering of online-game players,” The International Journal of Virtual Reality, vol. 6, no. 3, pp. 11–16, 2007.
  7. R. Bartle, “Hearts, clubs, diamonds, spades: players who suit MUDs,” The Journal of Virtual Environments, vol. 1, no. 1, 1996.
  8. M. Wattenberg and J. Kriss, “Designing for social data analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 4, pp. 549–557, 2006.
  9. I. Borg and P. Groenen, Modern Multidimensional Scaling: Theory and Applications, Springer Series in Statistics, Springer, New York, NY, USA, 2nd edition, 2005.
  10. Y. Ohsawa, N. E. Benson, and M. Yachida, “KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor,” in Proceedings of the IEEE International Forum on Research and Technology Advances in Digital Libraries (ADL '98), pp. 12–18, Santa Barbara, Calif, USA, April 1998.
  11. R. Thawonmas and K. Iizuka, “Haar wavelets for online-game player classification with dynamic time warping,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 12, no. 2, pp. 150–155, 2008.
  12. F. D. Fracchia, “Is visualization struggling under the myth of objectivity?,” in Proceedings of the 6th IEEE Visualization Conference (VIS '95), pp. 412–415, IEEE Computer Society Press, Atlanta, Ga, USA, October-November 1995.
  13. P. J. Somervuo, “Online algorithm for the self-organizing map of symbol strings,” Neural Networks, vol. 17, no. 8-9, pp. 1231–1239, 2004.
  14. J. D. Cohen, “Highlights: language- and domain-independent automatic indexing terms for abstracting,” Journal of the American Society for Information Science, vol. 46, no. 3, pp. 162–174, 1995.
  15. Y. Ohsawa and P. McBurney, Eds., Chance Discovery—Foundation and Its Applications, Y. Ohsawa and P. McBurney, Eds., Springer, New York, NY, USA, 2003.
  16. R. Thawonmas and K. Hata, “Aggregation of action symbol sub-sequences for discovery of online-game player characteristics using KeyGraph,” in Proceedings of the 4th International Conference on Entertainment Computing (ICEC '05), F. Kishino, Y. Kitamura, H. Kato, and N. Nagata, Eds., vol. 3711 of Lecture Notes in Computer Science, pp. 126–135, Sanda, Japan, September 2005.
  17. N. Okazaki and Y. Ohsawa, “Polaris: an integrated data miner for chance discovery,” in Proceedings of the 3rd International Workshop of Chance Discovery and Its Management in conjunction with International Human Conputer Interaction Conference (HCI '03), pp. 27–30, Crete, Greece, June 2003.
  18. A. Tveit, Ø. Rein, J. V. Iversen, and M. Matskin, “Scalable agent-based simulation of players in massively multiplayer online games,” in Proceedings of the 8th Scandinavian Conference on Artificial Intelligence (SCAI '03), pp. 80–89, Bergen, Norway, November 2003.
  19. W. White, C. Koch, N. Gupta, J. Gehrke, and A. Demers, “Database research opportunities in computer games,” SIGMOD Record, vol. 36, no. 3, pp. 7–13, 2007.
  20. N. Yee, “Motivations for play in online games,” Cyberpsychology & Behavior, vol. 9, no. 6, pp. 772–775, 2006.
  21. F. K.-P. Chan, A. W.-C. Fu, and C. Yu, “Haar wavelets for efficient similarity search of time-series: with and without time warping,” IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 3, pp. 686–705, 2003.
  22. C. C. Aggarwal and P. S. Bradley, “On the use of wavelet decomposition for string classification,” Data Mining and Knowledge Discovery, vol. 10, no. 2, pp. 117–139, 2005.
  23. The ICE Online Game, http://www.ice.ci.ritsumei.ac.jp/mmog.html.
  24. J. Tzeng, H. Lu, and W.-H. Li, “Multidimensional scaling for large genomic data sets,” BMC Bioinformatics, vol. 9, article 179, 2008.
  25. C. A. Ratanamahatana and E. Keogh, “Three myths about dynamic time warping,” in Proceedings of SIAM International Conference on Data Mining (SDM '05), pp. 506–510, Newport Beach, Calif, USA, April 2005.