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Mining Geophysical Data for Knowledge
October 1996 (vol. 11 no. 5)
pp. 34-44
ASCII Text
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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 Intelligent Systems, vol. 11, no. 5, pp. 34-44, October, 1996.
 
 
BibTex
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@article{ 10.1109/64.539015,
author = {Edmond Mesrobian and Richard Muntz and Eddie Shek and Siliva Nittel and Mark La Rouche and Marc Kriguer and Carlos Mechoso and John Farrara and Paul Stolorz and Hisashi Nakamura},
title = {Mining Geophysical Data for Knowledge},
journal ={IEEE Intelligent Systems},
volume = {11},
number = {5},
issn = {0885-9000},
year = {1996},
pages = {34-44},
doi = {http://doi.ieeecomputersociety.org/10.1109/64.539015},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Mining Geophysical Data for Knowledge
IS - 5
SN - 0885-9000
SP34
EP44
EPD - 34-44
A1 - Edmond Mesrobian,
A1 - Richard Muntz,
A1 - Eddie Shek,
A1 - Siliva Nittel,
A1 - Mark La Rouche,
A1 - Marc Kriguer,
A1 - Carlos Mechoso,
A1 - John Farrara,
A1 - Paul Stolorz,
A1 - Hisashi Nakamura,
PY - 1996
VL - 11
JA - IEEE Intelligent Systems
ER -
 

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.

    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 Intelligent Systems, vol. 11, no. 5, pp. 34-44, Oct. 1996, doi:10.1109/64.539015
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