DocumentCode
1996556
Title
Virtual astronomy, information technology, and the new scientific methodology
Author
Djorgovski, S.G.
Author_Institution
Div. of Phys., Math., & Astron., California Inst. of Technol., Pasadena, CA, USA
fYear
2005
fDate
4-6 July 2005
Firstpage
125
Lastpage
132
Abstract
All sciences, including astronomy, are now entering the era of information abundance. The exponentially increasing volume and complexity of modern data sets promises to transform the scientific practice, but also poses a number of common technological challenges. The virtual observatory concept is the astronomical community\´s response to these challenges: it aims to harness the progress in information technology in the service of astronomy, and at the same time provide a valuable testbed for information technology and applied computer science. Challenges broadly fall into two categories: data handling (or "data farming"), including issues such as archives, intelligent storage, databases, interoperability, fast networks, etc., and data mining, data understanding, and knowledge discovery, which include issues such as automated clustering and classification, multivariate correlation searches, pattern recognition, visualization in highly hyperdimensional parameter spaces, etc., as well as various applications of machine learning in these contexts. Such techniques are forming a methodological foundation for science with massive and complex data sets in general, and are likely to have a much broader impact on the modern society, commerce, information economy, security, etc. There is a powerful emerging synergy between the computationally enabled science and the science-driven computing, which will drive the progress in science, scholarship, and many other venues in the 21st century.
Keywords
astronomy computing; data handling; learning (artificial intelligence); applied computer science; automated classification; automated clustering; computationally enabled science; data farming; data handling; data mining; data understanding; highly hyperdimensional parameter spaces; information technology; intelligent storage; knowledge discovery; machine learning; massive complex data sets; modern data sets; multivariate correlation searches; pattern recognition; science-driven computing; scientific visualization; virtual astronomy; virtual observatory concept; Astronomy; Computer science; Data handling; Deductive databases; Information technology; Intelligent networks; Learning systems; Observatories; Space technology; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Architecture for Machine Perception, 2005. CAMP 2005. Proceedings. Seventh International Workshop on
Print_ISBN
0-7695-2255-6
Type
conf
DOI
10.1109/CAMP.2005.53
Filename
1508175
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