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Published 2010 | Published
Journal Article Open

The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

Abstract

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments that are vital to the analysis and visualization of complex datasets and images. One of the goals of this paper is to help bridge the gap between applied mathematics and artificial intelligence on the one side and astronomy on the other.

Additional Information

© 2010 Meyer Z. Pesenson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received 15 June 2009; Revised 21 October 2009; Accepted 12 January 2010. Academic Editor: Joshua S. Bloom. The authors would like to thank Alanna Connors for stimulating discussions and useful suggestions. They would also like to thank the referee for constructive suggestions that led to substantial improvements of the article. The first author would like to thank Michael Werner for support. This work was carried out with funding from the National Geospatial- Intelligence Agency University Research Initiative (NURI), Grant HM1582-08-1-0019, and support from NASA to the California Institute of Technology and the Jet Propulsion Laboratory.

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August 19, 2023
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