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Abstract

Scientific discoveries occur with iterations of theory, experiment, and analysis. But the methods that scientists use to go about their work are changing [1].

Experiment types are changing. Increasingly, experiment means computational experiment [2], as computers increase in speed, memory, and parallel processing capability. Laboratory experiments are becoming parallel as combinatorial experiments become more common.

Acquired datasets are changing. Both computer and laboratory experiments can produce large quantities of data where the time to analyze data can exceed the time to generate it. Data from experiments can come in surges where the analysis of each set determines the direction of the next experiments. The data generated by experiments may also be non-intuitive. For example, nanoscience is the study of materials whose properties may change greatly as their size is reduced [3]. Thus analyses may benefit from new ways to examine and interact with data.

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References

  1. Sims, J.S., George, W.L., Satterfield, S.G., Hung, H.K., Hagedorn, J.G., Ketcham, P.M., Griffin, T.J., Hagstrom, S.A., Franiatte, J.C., Bryant, G.W., Jaskolski, W., Martys, N.S., Bouldin, C.E., Simmons, V., Nicolas, O.P., Warren, J.A., am Ende, B.A., Koontz, J.E., Filla, B.J., Pourprix, V.G., Copley, S.R., Bohn, R.B., Peskin, A.P., Parker, Y.M., Devaney, J.E.: Accelerating scientific discovery through computation and visualization II. Journal of Research of the National Institute of Standards and Technology 107, 223–245 (2002)

    Google Scholar 

  2. Westmoreland, P.R., Kollman, P.A., Chaka, A.M., Cummings, P.T., Morokuma, K., Neurock, M., Stechel, E.B., Vashishta, P.: Application of Molecular and Materials Modeling. Technology Research Institute, World Technology Division. Kluwer Academic Publishers, Dordrecht (2002)

    Google Scholar 

  3. Alivisatos, A.P.: Semiconductor clusters, nanocrystals, and quantum dots. Science 271, 933–937 (1996)

    Article  Google Scholar 

  4. Moore, G.E.: Cramming more components onto integrated circuits. Electronics 38 (1965), http://www.intel.com/research/silicon/mooreslaw.htm

  5. Moore, G.E.: No exponential is forever.. but we can delay ‘forever’. Presented at: Int’l Solid State Circuits Conf. (ISSCC) (2003), http://www.intel.com/research/silicon/mooreslaw.htm

  6. Grosh, J., Kaluzniacki, R., Dongarra, J.: Transition of solving important problems (2004) (unpublished)

    Google Scholar 

  7. Salvator, D.: GPU wars heat up again. PC Magazine, 34–35 (2004), http://www.extremetech.com/6800

  8. Salvator, D.: Nvidia readies geforce 6800. Ziff-Davis online magazine: Extreme-Tech.com (2004), http://www.extremetech.com/article2/0,1558,1624009,00.asp

  9. Edwards, W.K.: Core Jini, 2nd edn. Prentice Hall PTR, Englewood Cliffs (2000)

    Google Scholar 

  10. Freeman, E., Hupfer, S., Arnold, K.: JavaSpaces Principles, Patterns, and Practice. Addison-Wesley, Reading (1999)

    Google Scholar 

  11. Gelernter, D.: Linda in context. Comm. of ACM 32, 444–458 (1984)

    Google Scholar 

  12. Gelernter, D.: Generative communication in Linda. ACM Trans. Prog. Lang. and Sys. 7, 80–112 (1985)

    Article  MATH  Google Scholar 

  13. Sun Microsystems, Jini Community: Surrogate project (2004), http://surrogate.jini.org

  14. George, W.L., Scott, J.: Screen saver science: Realizing distributed parallel computing with Jini and Javaspaces. In: Conf. on Parallel Architectures and Compilation Techniques (PACT 2002) (2002), http://math.nist.gov/mcsd/savg/papers/SSS_PACT2002.ps.gz

  15. Sherman, W.R., Craig, A.B.: Understanding Virtual Reality. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  16. van Dam, A., Forsberg, A.S., Laidlaw, D.H., LaViola Jr., J.J., Simpson, R.M.: Immersive VR for scientific visualization: A progress report. IEEE Computer Graphics and Applications 20, 26–52 (2000)

    Google Scholar 

  17. van Dam, A., Laidlaw, D.H., Simpson, R.M.: Experiments in immersive virtual reality for scientific visualization. Computers and Graphics 26, 535–555 (2002)

    Article  Google Scholar 

  18. Bryson, S.: Virtual reality in scientific visualization. Comm. ACM 39, 62–71 (1996)

    Article  Google Scholar 

  19. Card, S.K., Mackinley, J.D., Shneiderman, B.: Readings in Information Visualization. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  20. Kerighan, B.W., Pike, R.: The UNIX Programming Environment. Prentice Hall, Inc., Englewood Cliffs (1984)

    Google Scholar 

  21. Kelso, J., Arsenault, L., Satterfield, S., Kriz, R.: DIVERSE: A framework for building extensible and reconfigurable device independent virtual environments. In: Proc. IEEE Virtual Reality 2002 Conf. (2002), DIVERSE source code available at, http://diverse.sourceforge.net

  22. SGI: OpenGL PerformerTM (2004), http://www.sgi.com/software/performer

  23. SGI: Open InventorTM (2003), http://oss.sgi.com/projects/inventor

  24. IBM: OpenDX (2004), http://www.opendx.org

  25. Kitware Inc.: VTK: The Visualization Toolkit (2004), http://www.vtk.org

  26. Brady, R., Pixton, J., Baxter, G., Moran, P., Potter, C.S., Carragher, B., Belmont, A.: Crumbs: a virtual environment tracking tool for biological imaging. In: Proc. 1995 Biomedical Visualization (BioMedVis 1995), p. 18. IEEE Computer Society, Los Alamitos (1995)

    Chapter  Google Scholar 

  27. VRCO: CAVElib user manual (2003), http://www.vrco.com/CAVE_USER/index.html

  28. CAVEav: CAVE audio & video library (2004), http://www-fp.mcs.anl.gov/~judson/CAVEav/CAVEav.html

  29. Roussos, M., Johnson, A., Moher, T., Leigh, J., Vasilakis, C., Barnes, C.: Learning and building together in an immersive virtual world. Presence 8, 247–263 (1999)

    Article  Google Scholar 

  30. Leigh, J., Johnson, A.E., DeFanti, T.A., Brown, M.D.: A review of tele-immersive applications in the CAVE research network. In: VR, p. 180 (1999)

    Google Scholar 

  31. Leigh, J., Rajlich, P., Stein, R., Johnson, A.E., DeFanti, T.A.: LIMBO/VTK: A tool for rapid tele-immersive visualization. In: Proc. IEEE Visualizaton 1998 (1998), http://www.evl.uic.edu/cavern/cavernpapers/viz98/leigh_j.pdf

  32. Morgan, T., Kriz, R.D., Howard, S., Das-Neves, F., Kelso, J.: Extending the use of collaborative virtual environments for instruction to K-12 schools. In> >sight 1, 67–82 (2002), http://www.sv.vt.edu/future/cave/pub/kriz_ael/insight.pdf

    Google Scholar 

  33. Kriz, R.D., Farkas, D., Ray, A.A., Kelso, J., Flanery Jr., R.E.: Visual interpretation and analysis of HPC nanostructure models using shared virtual environments. In: Conf. Proc. High Performance Computing: Grand Challenges in Computer Simulations, The Society for Modeling and Simulation International (SCS), San Diego, California, pp. 127–135 (2003), http://www.jwave.vt.edu/~rkriz/Pubs/HPC_2003/hpc2003distribute.pdf

  34. Ray, A.A.: The collaborative toolkit for diverse (2003), http://www.sv.vt.edu/future/cave/software/D_collabtools/D_collabtools.html

  35. Rich, E., Knight, K.: Artificial Intelligence. McGraww-Hill, New York (1991)

    Google Scholar 

  36. Inselberg, A.: The plane with parallel coordinates. The Visual Computer 1 (1985)

    Google Scholar 

  37. Cheng, J., Hatzis, C., Hayashi, H., Krogel, M.A., Morishita, S., Page, D., Sese, J.: KDD Cup 2001 report. SIGKDD Explorations 3 (2002)

    Google Scholar 

  38. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kauffann, San Francisco (1999)

    Google Scholar 

  39. Cheeseman, P., Kelley, J., Self, M., Taylor, W., Freeman, D.: Autoclass: A Bayesian classification system. In: Proc. 5th Int’l Conf. on Machine Learning (1988)

    Google Scholar 

  40. Hagedorn, J.G., Devaney, J.E.: A genetic programming system with a procedural program representation. In: 2001 Genetic and Evolutionary Computation Conf. Late Breaking Papers (2001), http://math.nist.gov/mcsd/savg/papers/g2001.ps.gz

  41. Devaney, J.: The role of choice in discovery. In: Arikawa, S., Morishita, S. (eds.). LNCS, vol. 167. Springer, Heidelberg (2000)

    Google Scholar 

  42. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

  43. Aray, Y., Marquez, M., Rodríguez, J., Coll, S., Simón-Manso, Y., Gonzalez, C., Weitz, D.A.: Electrostatics for exploring the nature of water adsorption on the laponite sheets surface. J. Phys. Chem. B 107, 8946–8952 (2003)

    Google Scholar 

  44. Louis Pasteur: Lecture (1854), http://www.quotationspage.com/quotes/Louis_Pasteur/

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© 2005 Springer-Verlag Berlin Heidelberg

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Devaney, J.E. et al. (2005). Science at the Speed of Thought. In: Cai, Y. (eds) Ambient Intelligence for Scientific Discovery. Lecture Notes in Computer Science(), vol 3345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32263-4_1

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  • DOI: https://doi.org/10.1007/978-3-540-32263-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24466-0

  • Online ISBN: 978-3-540-32263-4

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