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Scientific data management in the coming decade

ACM SIGMOD RecordPublished 1 December 2005
Jim Gray, David T. Liu, M. A. Nieto‐Santisteban, Alexander S. Szalay, David J. DeWitt, Gerd Heber
Citations487
SJR quartileQ2
SJR score0.69
SNIP0.92

TL;DR

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.

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.

Keywords

Computer ScienceDecision Sciences