Scientific data analysis

Chandrika Kamath; Nikil Wale; George Karypis; Gaurav Pandey; Vipin Kumar; Krishna Rajan; Nagiza F. Samatova; Paul Breimyer; Guruprasad Kora; Chongle Pan; Srikanth Yoginath

doi:10.1201/9781420069815

Scientific data analysis

Chandrika Kamath, Nikil Wale, George Karypis, Gaurav Pandey, Vipin Kumar, Krishna Rajan, Nagiza F. Samatova, Paul Breimyer, Guruprasad Kora, Chongle Pan, Srikanth Yoginath

Systems and Decision Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Scopus citations

Abstract

The analysis of data is a key part of any scientific endeavor, as it leads to a better understanding of the world around us. With scientific data now being measured in terabytes and petabytes, this analysis is becoming quite challenging. In addition, the complexity of the data is increasing as well due to several factors such as improved sensor technologies and increased computing power. This complexity can take various forms such as multisensor, multispectral, multiresolution data, spatio-temporal data, high-dimensional data, structured and unstructured mesh data from simulations, data contaminated with different types of noise, three-dimensional data, and so on.

Original language	English
Title of host publication	Scientific Data Management
Subtitle of host publication	Challenges, Technology, and Deployment
Publisher	CRC Press
Pages	281-323
Number of pages	43
ISBN (Electronic)	9781420069815
ISBN (Print)	9781420069808
DOIs	https://doi.org/10.1201/9781420069815
State	Published - Jan 1 2009

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AU - Kamath, Chandrika

AU - Wale, Nikil

AU - Karypis, George

AU - Pandey, Gaurav

AU - Kumar, Vipin

AU - Rajan, Krishna

AU - Samatova, Nagiza F.

AU - Breimyer, Paul

AU - Kora, Guruprasad

AU - Pan, Chongle

AU - Yoginath, Srikanth

PY - 2009/1/1

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AB - The analysis of data is a key part of any scientific endeavor, as it leads to a better understanding of the world around us. With scientific data now being measured in terabytes and petabytes, this analysis is becoming quite challenging. In addition, the complexity of the data is increasing as well due to several factors such as improved sensor technologies and increased computing power. This complexity can take various forms such as multisensor, multispectral, multiresolution data, spatio-temporal data, high-dimensional data, structured and unstructured mesh data from simulations, data contaminated with different types of noise, three-dimensional data, and so on.

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BT - Scientific Data Management

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Scientific data analysis

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