|
Published Articles >> Table of Contents >> Abstract
October 1996 (Vol. 11, No. 5)
pp. 34-44
Mining Geophysical Data for Knowledge
Edmond Mesrobian
Richard Muntz
Eddie Shek
Siliva Nittel
Mark La Rouche
Marc Kriguer
Carlos Mechoso
John Farrara
Paul Stolorz
Hisashi Nakamura
Full Article Text:
   
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/64.539015
Send link to a friend
| Abstract |
|
Oasis is a flexible, extensible, and seamless environment for scientific data analysis, knowledge discovery, visualization, and collaboration. The authors describe how Oasis can help explore data analysis and data mining of spatio-temporal phenomena from large geophysical data sets. Exploratory data mining and analysis for scientific hypothesis testing or phenomenon detection is an iterative, successive-refinement process. Scientists apply a preliminary model on the data and then use the outcome of a series of experiments to refine the model and methodology. They repeat this process until they either drop the hypothesis or refine it into one that is consistent with the collected data. For such a research approach to be practical, scientists need a powerful system that supports easy formulation and execution of powerful queries and discriminant functions against the database, a natural representation of the relationships of the scientific domain of interest (for example, in the natural domains of space and time but possibly in the frequency domain, as well), and efficient execution of these queries without requiring the scientists to be aware of the storage structures and processing strategies involved. We are developing Oasis (open architecture scientific information system) to be such a system. In this article, we explain how scientists can use this flexible, extensible, and seamless computing environment for scientific data analysis, knowledge discovery, visualization, and collaboration.
|
 References
|
[1] E. Mesrobian, R. Muntz, E. Shek, S. Nittel, M. LaRouche, and M. Kriguer, "OASIS: An Open Architecture Scientific Information System," Proc. RIDE '96,New Orleans, IEEE Press, 1996.
[2] A.S. Jacobson, A.L. Berkin, and M.N. Orton, "LinkWinds, Interactive Scientific Data Analysis and Visualization," Comm. ACM, Vol. 37, No. 4, Apr. 1994, pp. 42-52.
[3] E.C. Shek, E. Mesrobian, and R.R. Muntz, "On Heterogeneous Distributed Geoscientific Query Processing," Proc. Sixth Int'l Workshop Research Issues in Data Engineering, IEEE CS Press, 1996, pp. 98-106.
[4] R.M. Soley ed., Object Management Architecture Guide, 2nd ed., Object Management Group, Framingham, Mass., 1992.
[5] K. Beuhler and L. McKee eds., The OpenGIS Guide: Introduction to Interoperable Geoprocessing, Open GIS Consortium Inc., Waylard, Mass., 1996.
[6] G. Graefe,, "Volcano: An Extensible and Parallel Query Evaluation System," IEEE Trans. Knowledge and Data Engineering, Vol. 6, No. 1, Feb. 1994, pp. 120-135.
[7] S. Chawathe et al., "The TSIMMIS Project: Integration of Heterogeneous Information Sources," Proc. IPSJ, Information Processing Soc. of Japan, Tokyo, 1994, pp. 7-18.
[8] C.R. Mechoso, S.W. Lyons, and J.A. Spahr, "The Impact of Sea Surface Temperature Anomalies on the Rainfall over Northeast Brazil," J. Climate, Vol. 3, No. 8, Aug. 1990, pp. 812-826.
Additional References
[1] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc. 1995 Int'l Conf. Data Eng., pp. 3-14, Mar. 1995.
[2] E. Mesrobian et al., "Extracting Spatio-Temporal Patterns from Geoscience Datasets," Proc. IEEE Workshop Visualization and Machine Vision, IEEE CS Press, 1994, pp. 92-103.
[3] R. Duda and P. Hart, Pattern Classification and Scene Analysis, John Wiley&Sons, New York, 1973.
[4] A. Gelman et al., Bayesian Data Analysis, Chapman&Hall, London, 1995.
|
 Additional Information
|
Citation:
Edmond Mesrobian, Richard Muntz, Eddie Shek, Siliva Nittel, Mark La Rouche, Marc Kriguer, Carlos Mechoso, John Farrara, Paul Stolorz, Hisashi Nakamura,
"Mining Geophysical Data for Knowledge,"
IEEE Expert: Intelligent Systems and Their Applications,
vol. 11,
no. 5,
pp. 34-44,
Oct.,
1996
|
|
RSS Feed