AI SearchAnalyzing email transactions
Call Number: AIT Thesis no.IM-09-08 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. IM-09-08Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2009Description: 51 p. : illSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2009 Summary: In a complex Email system with plenty of messages, each user can receive dozens of messages every day. How to recognize which messages should be read first and how to find important or prominent people in the system without knowing the messages' content. This study focuses on Email transactions that show relationships among entities via messages' transference in the system to discover important messages and important people from email log. Important messages are messages that contain interesting information or impoliant contents and important people are those who have strong effects on the community via sending and receiving several messages. Two data structures are introduced for this task. The Email transaction multi-digraph is used to represent the email transactions which are encoded in the mail log. Whereas, the messages' flow is used by the Scoring Model to calculate the scores for email messages based on types of messages (original, forward, or reply). The results of the Scoring Model are used to determine important messages and important people. Some experiments were carried out to verify the methodology and the results showed that this model's result is acceptable.
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management, School of Engineering and Technology
Thesis (M.Eng.) - Asian Institute of Technology, 2009
In a complex Email system with plenty of messages, each user can receive dozens of messages every day. How to recognize which messages should be read first and how to find important or prominent people in the system without knowing the messages' content. This study focuses on Email transactions that show relationships among entities via messages' transference in the system to discover important messages and important people from email log. Important messages are messages that contain interesting information or impoliant contents and important people are those who have strong effects on the community via sending and receiving several messages. Two data structures are introduced for this task. The Email transaction multi-digraph is used to represent the email transactions which are encoded in the mail log. Whereas, the messages' flow is used by the Scoring Model to calculate the scores for email messages based on types of messages (original, forward, or reply). The results of the Scoring Model are used to determine important messages and important people. Some experiments were carried out to verify the methodology and the results showed that this model's result is acceptable.
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