SchedMail: Sender-Assisted Message Delivery Scheduling to Reduce Time-Fragmentation

Although early efforts aimed at dealing with large amounts of emails focused on filtering out spam, there is growing interest in prioritizing non-spam emails, with the objective of reducing information overload and time fragmentation experienced by recipients. However, most existing approaches place the burden of classifying emails exclusively on the recipients' side, either directly or through recipients' email service mechanisms. This disregards the fact that senders typically know more about the nature of the contents of outgoing messages before the messages are read by recipients. This thesis presents mechanisms collectively called SchedMail which can be added to popular email clients, to shift a part of the user efforts and computational resources required for email prioritization to the senders' side. Particularly, senders declare the urgency of their messages, and recipients specify policies about when different types of messages should be delivered. Recipients also judge the accuracy of sender-side urgency, which becomes the basis for learned reputations of senders; these reputations are then used to interpret urgency declarations from the recipients' perspectives. In order to experimentally evaluate the proposed mechanisms, a proof-of-concept prototype was implemented based on a popular open source email client K-9 Mail. By comparing the amount of email interruptions experienced by recipients, with and without SchedMail, the thesis concludes that SchedMail can effectively reduce recipients' time fragmentation, without placing demands on email protocols or adding significant computational overhead

Learning from textual data streams for detecting email spam

This master thesis introduces a method for the detecting email spam through the translation problem in incremental learning of the time series. Common spam detection systems mainly use methods of supervised learning (naive Bayesian classifier, decision trees), while in the master’s thesis presents the classification by using the methods of data stream mining. For learning sets, we also choose the attributes that do not contain personal data and which are not required to obtain the consent of the sender or the recipient (attributes consist the envelope part of e-mail). With the help of algorithms for learning from data streams (VFDT, cVFDT) we used the electronic sequence of messages as text data stream. The results were compared with the traditional spam detection methods and they show that traditional spam detection methods have higher accuracy compared to algorithms for learning from data stream and therefore are not suitable for detecting email spam

Learning from textual data streams for detecting email spam

This master thesis introduces a method for the detecting email spam through the translation problem in incremental learning of the time series. Common spam detection systems mainly use methods of supervised learning (naive Bayesian classifier, decision trees), while in the master’s thesis presents the classification by using the methods of data stream mining. For learning sets, we also choose the attributes that do not contain personal data and which are not required to obtain the consent of the sender or the recipient (attributes consist the envelope part of e-mail). With the help of algorithms for learning from data streams (VFDT, cVFDT) we used the electronic sequence of messages as text data stream. The results were compared with the traditional spam detection methods and they show that traditional spam detection methods have higher accuracy compared to algorithms for learning from data stream and therefore are not suitable for detecting email spam