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. In particular we have demonstrated the proposed framework in the scenario of handling large scale data generated from biomolecular simulations in a multiinstitutional global collaboration. The framework has extended the ability of the Python problem solving environment to manage data files and metadata associated with simulations. We provide a transparent and seamless environment for user submitted code to analyse and post-process data stored in the framework. Based on this scenario we have further enhanced and extended the framework to deal with the more generic case of enabling any existing data file to be post processed from any .NET enabled programming language.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:440419 |
| Date | January 2006 |
| Creators | Johnston, Steven |
| Contributors | Cox, Simon ; Fanghor, Hans |
| Publisher | University of Southampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://eprints.soton.ac.uk/65549/ |
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