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- Information flow identification in large email datasets
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Information flow identification in large email datasets Akuney, Arseniy
Abstract
Identifying information flow in emails is an important, yet challenging task. In this work we investigate several algorithms for identifying similar sentences in large email datasets, as well as an algorithm for reconstructing threads from unstructured emails. We present a detailed evaluation of each algorithm in terms of accuracy and time performance. We also investigate the usage of cloud computing in order to increase computational efficiency and make information discovery usable in real time.
Item Metadata
| Title |
Information flow identification in large email datasets
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| Creator | |
| Publisher |
University of British Columbia
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| Date Issued |
2011
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| Description |
Identifying information flow in emails is an important, yet challenging task. In this work we investigate several algorithms for identifying similar sentences in large email datasets, as well as an algorithm for reconstructing threads from unstructured emails. We present a detailed evaluation of each algorithm in terms of accuracy and time performance. We also investigate the usage of cloud computing in order to increase computational efficiency and make information discovery usable in real time.
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| Genre | |
| Type | |
| Language |
eng
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| Date Available |
2011-12-23
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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| DOI |
10.14288/1.0052116
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| URI | |
| Degree | |
| Program | |
| Affiliation | |
| Degree Grantor |
University of British Columbia
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| Graduation Date |
2012-05
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| Campus | |
| Scholarly Level |
Graduate
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International