Abstract
The rapid increase in the volume of scientific data sets has resulted in distributed data information systems applicable to Earth system science. Such a system should help users to locate data sets, to provide preliminary research results quickly and to support data deliveries under users’ request. At George Mason University, we have been developing a data information system with both search and analysis components. In this system, three phases of data accesses are supported: phase one for meta-data search; phase two for on-line data analysis; and phase three for data ordering. For large volumes of data, searching on meta-data only will not be adequate. Scientists often need to search for data based on actual data values. This is a particular kind of data mining, which searches for data sets based on data content.
In this chapter, we first describe the system architecture. We then develop the concept of a data pyramid model and propose a histogram clustering technique for content-based searches. We use the model and the related technique to answer content-based queries approximately but efficiently. We will also describe our prototypes that integrate the content-based searches into a data information system.
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Yang, R., Kafatos, M., Yang, KS., Wang, X.S. (2001). Data Mining in Integrated Data Access and Data Analysis Systems. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. Massive Computing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1733-7_11
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DOI: https://doi.org/10.1007/978-1-4615-1733-7_11
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