Article thumbnail

Managing scientific data

By Anastasia Ailamaki, Vasiliki Kantere and Debabrata Dash


Data-oriented scientific processes depend on fast, accurate analysis of experimental data generated through empirical observation and simulation. However, scientists are increasingly overwhelmed by the volume of data produced by their own experiments. With improving instrument precision and the complexity of the simulated models, data overload promises to only get worse. The inefficiency of existing database management systems (DBMSs) for addressing the requirements of scientists has led to many application-specific systems. Unlike their general-purpose counterparts, these systems require more resources, hindering reuse of knowledge. Still, the data-management community aspires to general-purpose scientific data management. Here, we explore the most important requirements of such systems and the techniques being used to address them

Publisher: 'Association for Computing Machinery (ACM)'
Year: 2010
DOI identifier: 10.1145/1743546.1743568
OAI identifier:

Suggested articles

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.