Managing large volumes of distributed scientific data
The ability to store large volumes of data is increasing faster than processing power. Some existing data management methods often result in data loss, inaccessibility or repetition of simulations. We propose a framework which promotes collaboration and simplifies data management. We propose an imp...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
2008.
|
| Subjects: | |
| Online Access: | Get fulltext |
| LEADER | 00926 am a22001453u 4500 | ||
|---|---|---|---|
| 001 | 43706 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Johnston, S.J. |e author |
| 700 | 1 | 0 | |a Fangohr, H. |e author |
| 700 | 1 | 0 | |a Cox, S.J. |e author |
| 245 | 0 | 0 | |a Managing large volumes of distributed scientific data |
| 260 | |c 2008. | ||
| 856 | |z Get fulltext |u https://eprints.soton.ac.uk/43706/1/John_08.pdf | ||
| 520 | |a The ability to store large volumes of data is increasing faster than processing power. Some existing data management methods often result in data loss, inaccessibility or repetition of simulations. We propose a framework which promotes collaboration and simplifies data management. We propose an implementation independent framework to promote collaboration and data management across a distributed environment. We discuss the framework features using a .NET Framework implementation and demonstrate the capabilities through a simple example. | ||
| 655 | 7 | |a Article | |