Managing large volumes of distributed scientific data

Steven Johnston; Hans Fangohr; Simon J. Cox Cox

doi:10.1007/978-3-540-69389-5_39

Managing large volumes of distributed scientific data

Steven Johnston
, Hans Fangohr
, Simon J. Cox Cox

University of Southampton

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	Computational Science - ICCS 2008
Subtitle of host publication	Lecture Notes in Computer Science
Editors	M. Bubak, G. D. van Albada, J. Dongarra, P. M. A. Sloot
Pages	339-348
Number of pages	10
Volume	5103
ISBN (Electronic)	9783540693895
DOIs	https://doi.org/10.1007/978-3-540-69389-5_39
Publication status	Published - 23 Jun 2008
Event	International Conference on Computational Science - Krakow, Poland Duration: 23 Jun 2008 → 25 Jun 2008 https://link.springer.com/book/10.1007/978-3-540-69389-5

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
ISSN (Print)	0302-9743

Conference

Conference	International Conference on Computational Science
Abbreviated title	ICCS 2008
Country/Territory	Poland
City	Krakow
Period	23/06/08 → 25/06/08
Internet address	https://link.springer.com/book/10.1007/978-3-540-69389-5

Keywords

File Object Model
Data management
database

Access to Document

10.1007/978-3-540-69389-5_39

Cite this

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title = "Managing large volumes of distributed scientific data",

abstract = "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.",

keywords = "File Object Model, Data management, database",

author = "Steven Johnston and Hans Fangohr and Cox, \{Simon J. Cox\}",

year = "2008",

month = jun,

day = "23",

doi = "10.1007/978-3-540-69389-5\_39",

language = "English",

isbn = "9783540693888",

volume = "5103",

series = "Lecture Notes in Computer Science",

publisher = "Springer Verlag",

pages = "339--348",

editor = "M. Bubak and \{van Albada\}, \{G. D. \} and J. Dongarra and Sloot, \{P. M. A.\}",

booktitle = "Computational Science - ICCS 2008",

note = "International Conference on Computational Science, ICCS 2008 ; Conference date: 23-06-2008 Through 25-06-2008",

url = "https://link.springer.com/book/10.1007/978-3-540-69389-5",

}

Johnston, S, Fangohr, H & Cox, SJC 2008, Managing large volumes of distributed scientific data. in M Bubak, GD van Albada, J Dongarra & PMA Sloot (eds), Computational Science - ICCS 2008: Lecture Notes in Computer Science. vol. 5103, Lecture Notes in Computer Science, pp. 339-348, International Conference on Computational Science, Krakow, Poland, 23/06/08. https://doi.org/10.1007/978-3-540-69389-5_39

Managing large volumes of distributed scientific data. / Johnston, Steven; Fangohr, Hans; Cox, Simon J. Cox.
Computational Science - ICCS 2008: Lecture Notes in Computer Science. ed. / M. Bubak; G. D. van Albada; J. Dongarra; P. M. A. Sloot. Vol. 5103 2008. p. 339-348 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Managing large volumes of distributed scientific data

AU - Johnston, Steven

AU - Fangohr, Hans

AU - Cox, Simon J. Cox

PY - 2008/6/23

Y1 - 2008/6/23

N2 - 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.

AB - 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.

KW - File Object Model

KW - Data management

KW - database

U2 - 10.1007/978-3-540-69389-5_39

DO - 10.1007/978-3-540-69389-5_39

M3 - Conference contribution

SN - 9783540693888

VL - 5103

T3 - Lecture Notes in Computer Science

SP - 339

EP - 348

BT - Computational Science - ICCS 2008

A2 - Bubak, M.

A2 - van Albada, G. D.

A2 - Dongarra, J.

A2 - Sloot, P. M. A.

T2 - International Conference on Computational Science

Y2 - 23 June 2008 through 25 June 2008

ER -

Managing large volumes of distributed scientific data

Abstract

Publication series

Conference

Keywords

Access to Document

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Cite this