Scientific data management in the coming decade
By Jim Gray, David T. Liu, M. Nieto-Santisteban, A. Szalay, David J. DeWitt, Gerd Heber. SGMD, 2005
553 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/3a31e91357b9fe0f2ba3a62a3e8ec592a9a24427
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- J. Cheney
- Adriane P. Chapman
- Christine L. Borgman
- Geoffrey C. Bowker
- Paul N. Edwards
- Matthew S. Mayernik
- Archer L. Batcheller
- Peter Buneman
In Information Systems and Management
- J. Cheney
- Christine L. Borgman
- Geoffrey C. Bowker
- Paul N. Edwards
- Matthew S. Mayernik
- Archer L. Batcheller
Citations in the corpus
- The conundrum of sharing research data (2012, methodology): “This is the claimed “fourth paradigm,” that computational science constitutes a new set of methods beyond empiricism, theory, and simulation (Bell, Hey & Szalay, 2009; Gray et al., 2005; Hey, T. et al., 2009).”
- Provenance management in curated databases (2006, background): “This poses new challenges for database technology, many of them to do with scale and distributed processing [13].”
- Science friction: Data, metadata, and collaboration (2011, background): “For example, Gray et al. (2005) state that successful scientific analysis of large datasets depends crucially on developing ‘extensive metadata and metadata standards that will make it easy to discover what data exist, make it easy for people and programs to understand the data, and make it easy to…”