Scientific data management in the coming decade

Author:

Gray Jim1,Liu David T.2,Nieto-Santisteban Maria3,Szalay Alex3,DeWitt David J.4,Heber Gerd5

Affiliation:

1. Microsoft

2. Berkeley

3. Johns Hopkins University

4. Wisconsin

5. Cornell

Abstract

Scientific instruments and computer simulations are creating vast data stores that require new scientific methods to analyze and organize the data. Data volumes are approximately doubling each year. Since these new instruments have extraordinary precision, the data quality is also rapidly improving. Analyzing this data to find the subtle effects missed by previous studies requires algorithms that can simultaneously deal with huge datasets and that can find very subtle effects --- finding both needles in the haystack and finding very small haystacks that were undetected in previous measurements.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems,Software

Reference13 articles.

1. {fr1} Committee on Data Management Archiving and Computing (CODMAC) Data Level Definitions http://science.hq.nasa.gov/research/earth_science_formats.html {fr1} Committee on Data Management Archiving and Computing (CODMAC) Data Level Definitions http://science.hq.nasa.gov/research/earth_science_formats.html

2. {fr2} http://hdf.ncsa.uiuc.edu/HDF5/ {fr2} http://hdf.ncsa.uiuc.edu/HDF5/

3. {fr3} http://my.unidata.ucar.edu/content/software/netcdf/ {fr3} http://my.unidata.ucar.edu/content/software/netcdf/

4. {fr4} http://fits.gsfc.nasa.gov/ {fr4} http://fits.gsfc.nasa.gov/

5. {fr5} http://vizier.u-strasbg.fr/doc/UCD.htx {fr5} http://vizier.u-strasbg.fr/doc/UCD.htx

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