Abstract
We describe a preliminary implementation of a data analysis tool that can characterize features in large scientific datasets. There are two primary challenges in making such a tool both general and practical: first, the definition of an interesting feature changes from domain to domain; second, scientific data varies greatly in format and structure. Our solution uses a hierarchical feature ontology that contains a base layer of objects that violate basic continuity and smoothness assumptions, and layers of higher-order objects that violate the physical laws of specific domains. Our implementation exploits the metadata facilities of the SAF data access libraries in order to combine basic mathematics subroutines smoothly and handle data format translation problems automatically. We demonstrate the results on real-world data from deployed simulators.
Supported by the DOE ASCI program through a Level 3 grant from Sandia National Laboratories, and a Packard Fellowship in Science and Engineering.
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References
M. Berthold and D. Hand, editors. Intelligent Data Analysis: An Introduction. Springer-Verlag, 2000.
K. Bowyer, L. Hall, N. Chawla, T. Moore, and W. Kegelmeyer. A parallel decision tree builder for mining very large visualization datasets. In Proceedings of the IEEE 2000 Conference on Systems, Man, and Cybernetics, October 2000.
S. Brown and D. Braddy. PDBLib user’s manual. Technical Report M270, Rev. 2, Lawrence Livermore National Laboratory, January 1993.
S. A. Brown, M. Folk, G. Goucher, and R. Rew. Software for portable scientific data management. Computers in Physics, 7(3):304–308, 1993.
S. P. Brumby, J. Theiler, S. Perkins, N. Harvey, J. J. Szymanski, J. J. Bloch,, and M. Mitchell. Investigation of image feature extraction by a genetic algorithm. In Proceedings of SPIE, 1999.
H. Edelsbrunner and E. Muecke. Three-dimensional alpha shapes. ACM Transactions on Graphics, 13(1):43–72, 1994.
W. H. et al. A lattice model for data display. In Proceedings IEEE Visualization, pages 310–317, 1994.
R. Haber, B. Lucas, and N. Collins. A data model for scientific visualization with provisions for regular and irregular grids. In Proceedings IEEE Visualization, pages 298–305, 1991.
L. Hall, K. Bowyer, N. Chawla, T. Moore, and W. P. Kegelmeyer. AVATAR-adaptive visualization aid for touring and recovery. Sandia Report SAND2000-8203, Sandia National Laboratories, January 2000.
M. C. Miller, J. F. Reus, R. P. Matzke, W. J. Arrighi, L. A. Schoof, R. T. Hitt, and P. K. Espen. Enabling interoperation of high performance, scientific computing applications: Modeling scientific data with the sets & fields (SAF) modeling system. In International Conference on Computational Science (ICCS-2001), 2001.
F. P. Preparata and M. I. Shamos. Computational Geometry: An Introduction. Springer-Verlag, New York, 1985.
V. Robins, J. Meiss, and E. Bradley. Computing connectedness: An exercise in computational topology. Nonlinearity, 11:913–922, 1998.
V. Robins, J. Meiss, and E. Bradley. Computing connectedness: Disconnectedness and discreteness. Physica D, 139:276–300, 2000.
L. Schoof. The ASCI data models and formats (DMF) effort: A comprehensive approach to interoperable scientific data management and analysis. In 4th Symposium on Multidisciplinary Applications and Interoperable Computing, Dayton, OH, August 2000.
D. Wells, E. Greisen, and R. Harten. FITS: A flexible image transport system. Astronomy and Astrophysics Supplement Series, 44:363–370, 1981.
K. Yip and F. Zhao. Spatial aggregation: Theory and applications. Journal of Artificial Intelligence Research, 5:1–26, 1996.
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Bradley, E., Collins, N., Kegelmeyer, W.P. (2001). Feature Characterization in Scientific Datasets. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds) Advances in Intelligent Data Analysis. IDA 2001. Lecture Notes in Computer Science, vol 2189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44816-0_1
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DOI: https://doi.org/10.1007/3-540-44816-0_1
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