Dynamic Data Sharing from Large Data Sources

Fiona McNeill; Andriana Gkaniatsou; Alan Bundy

Dynamic Data Sharing from Large Data Sources

Fiona McNeill, Andriana Gkaniatsou, Alan Bundy

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

Abstract / Description of output

This paper outlines our work on facilitating fast, effective, on-demand data sharing during emergency response situations. A lazy approach to matching is taken, matching only when and what is necessary for the immediate situation. This allows us to deal with data from large sources represented in different formats. This is a position paper, describing how we are adapting well-developed matching techniques to be appropriate for a different domain, explaining the key challenges and differences of this domain, and describing what has been revealed by the
data analysis we have performed thus far.

Original language	English
Title of host publication	Proceedings of the 11th International ISCRAM Conference
Number of pages	7
Publication status	Published - 2014

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Mcneill Bundy 2004Accepted author manuscript, 218 KB

http://asio.bbn.com/lhd+semquant2012/lhd12_submission_4.pdf

Interpreting and integrating mismatched data on the fly
Bundy, A. & Mcneill, F.
EPSRC
30/05/12 → 29/05/13
Project: Research

Cite this

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title = "Dynamic Data Sharing from Large Data Sources",

abstract = "This paper outlines our work on facilitating fast, effective, on-demand data sharing during emergency response situations. A lazy approach to matching is taken, matching only when and what is necessary for the immediate situation. This allows us to deal with data from large sources represented in different formats. This is a position paper, describing how we are adapting well-developed matching techniques to be appropriate for a different domain, explaining the key challenges and differences of this domain, and describing what has been revealed by thedata analysis we have performed thus far.",

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AU - Gkaniatsou, Andriana

AU - Bundy, Alan

PY - 2014

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AB - This paper outlines our work on facilitating fast, effective, on-demand data sharing during emergency response situations. A lazy approach to matching is taken, matching only when and what is necessary for the immediate situation. This allows us to deal with data from large sources represented in different formats. This is a position paper, describing how we are adapting well-developed matching techniques to be appropriate for a different domain, explaining the key challenges and differences of this domain, and describing what has been revealed by thedata analysis we have performed thus far.

M3 - Conference contribution

BT - Proceedings of the 11th International ISCRAM Conference

ER -

Dynamic Data Sharing from Large Data Sources

Abstract / Description of output

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Projects

Interpreting and integrating mismatched data on the fly

Cite this