Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Database Use in Science Applications

  • Amarnath GuptaEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1276


A science application is any application where a natural, social or engineering problem is investigated.

The Problem

Many science applications are data intensive. Scientific experiments produce large volumes of complex data, and have a dire need to create persistent repositories for their data and knowledge. It would seem natural that data management systems and technology will be heavily used in science. And yet, scientists traditionally do not use database management systems, and often develop home-grown solutions, or file-based software for their complex data management needs. Clearly, there is a gap between scientists’; intended use of data and what current data management systems provide.


There are many reasons, both technical and non-technical, that explain why science users do not use data management systems for their applications. A recent study [ 3] highlights a number of factors scientists have cited. Others [ 2, 4, 6] have analyzed different reasons why...
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Altintas I, Berkley C, Jaeger E, Jones M, Ludäscher B, Mock S. Kepler: an extensible system for design and execution of scientific workflows. In: Proceedings of the 16th International Conference Scientific and Statistical Database Management; 2004. p. 423–4.Google Scholar
  2. 2.
    Buneman P. Why scientists Don’t use databases? NeSC presentation. 2002. Available from www.nesc.ac.uk/talks/opening/no_use.pdf
  3. 3.
    Gray J, Liu DT, Nieto-Santisteban MA, Szalay AS, Heber G, DeWitt D. Scientific data management in the coming decade. ACM SIGMOD Rec. 2005;34(4):35–41.CrossRefGoogle Scholar
  4. 4.
    Liebman MJ. Data management systems: science versus technology? OMICS J Integr Biol. 2003;7(1):67–9.CrossRefGoogle Scholar
  5. 5.
    Livny M, Ramakrishnan R, Beyer K, Chen G, Donjerkovic D, Lawande S, Myllymaki J, Wenger K. DEVise: integrated querying and visual exploration of large datasets. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, Tucson; 1997. p. 301–12.Google Scholar
  6. 6.
    Maier D. Will database systems fail bioinformatics, too? OMICS J Integr Biol. 2003;7(1):71–3.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.San Diego Supercomputer CenterUniversity of California San DiegoLa JollaUSA

Section editors and affiliations

  • Amarnath Gupta
    • 1
  1. 1.San Diego Supercomputer CenterUniv. of California San DiegoLa JollaUSA