Data signatures and visualization of scientific data sets
Pak Chung Wong; Foote, H.; Leung, R.; Adams, D.; Thomas, J.
Computer Graphics and Applications, IEEE
Volume 20, Issue 2, Mar/Apr 2000 Page(s):12 - 15
Digital Object Identifier 10.1109/38.824451
Summary:Today, as data sets used in computations grow in size and
complexity, the technologies developed over the years to deal with
scientific data sets have become less efficient and effective. Many
frequently used operations, such as eigenvector computation, could
quickly exhaust our desktop workstations once the data size reaches
certain limits. On the other hand, the high-dimensional data sets we
collect every day don't relieve the problem. Many conventional metric
designs that build on quantitative or categorical data sets cannot be
applied directly to heterogeneous data sets with multiple data types.
While building new machines with more resources might conquer the data
size problems, the complexity of today's computations requires a new
breed of projection techniques to support analysis of the data and
verification of the results. We introduce the concept of a data
signature, which captures the essence of a scientific data set in a
compact format, and use it to conduct analysis as if using the original.
A time-dependent climate simulation data set demonstrates our approach
and presents the results
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