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

The characterisation of engineering activity through email communication and content dynamics, for support of engineering project management

Significant challenge exists in the effective monitoring and management of engineering design and development projects. Due to traits such as contextual variation and scale, detailed understanding of engineering projects and activity are difficult to form, with monitoring hence reliant on interpretation of managerial personnel and adherence to defined performance indicators. This paper presents a novel approach to the quantitative monitoring and analysis of engineering activity through computational topic identification and analysis of low-level communication data. Through three metrics of communication activity, this approach enables detailed detection and tracking of activity associated with specific project work areas. By application to 11,832 emails within two industry email corpora, this work identifies four distinct patterns in activity, and derives seven characteristics of communication activity within engineering design and development. Patterns identified are associated with background discussion, focused working, and the appearance of issues, supporting detailed managerial understanding. Characteristics identified relate to through-process norms against which a manager may compare and assess. Such project-specific information extends the ability of managers to understand the activity within their specific project scenario. Through detailed description of activity and its characteristics, in tandem with existing toolsets, a manager may be supported in their interpretation and decision-making processes.</jats:p

Novel graph analytics for enhancing data insight

Graph analytics is a fast growing and significant field in the visualization and data mining community, which is applied on numerous high-impact applications such as, network security, finance, and health care, providing users with adequate knowledge across various patterns within a given system. Although a series of methods have been developed in the past years for the analysis of unstructured collections of multi-dimensional points, graph analytics has only recently been explored. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the graph mining algorithms, but also producing effective graph visualizations in order to enhance human perception. The current thesis deals with the investigation of novel methods for graph analytics, in order to enhance data insight. Towards this direction, the current thesis proposes two methods so as to perform graph mining and visualization. Based on previous works related to graph mining, the current thesis suggests a set of novel graph features that are particularly efficient in identifying the behavioral patterns of the nodes on the graph. The specific features proposed, are able to capture the interaction of the neighborhoods with other nodes on the graph. Moreover, unlike previous approaches, the graph features introduced herein, include information from multiple node neighborhood sizes, thus capture long-range correlations between the nodes, and are able to depict the behavioral aspects of each node with high accuracy. Experimental evaluation on multiple datasets, shows that the use of the proposed graph features for the graph mining procedure, provides better results than the use of other state-of-the-art graph features. Thereafter, the focus is laid on the improvement of graph visualization methods towards enhanced human insight. In order to achieve this, the current thesis uses non-linear deformations so as to reduce visual clutter. Non-linear deformations have been previously used to magnify significant/cluttered regions in data or images for reducing clutter and enhancing the perception of patterns. Extending previous approaches, this work introduces a hierarchical approach for non-linear deformation that aims to reduce visual clutter by magnifying significant regions, and leading to enhanced visualizations of one/two/three-dimensional datasets. In this context, an energy function is utilized, which aims to determine the optimal deformation for every local region in the data, taking the information from multiple single-layer significance maps into consideration. The problem is subsequently transformed into an optimization problem for the minimization of the energy function under specific spatial constraints. Extended experimental evaluation provides evidence that the proposed hierarchical approach for the generation of the significance map surpasses current methods, and manages to effectively identify significant regions and deliver better results. The thesis is concluded with a discussion outlining the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces

Email Communities of Interest

Email has become an integral and sometimes overwhelming part of users ’ personal and professional lives. In this paper, we measure the flow and frequency of user email toward the identification of communities of interest (COI)–groups of users that have a common bond. If detectable, such associations will be useful in automating email management, e.g., topical classification, flagging important missives, and SPAM mitigation. An analysis of a large corpus of university email is used to drive the generation and validation of algorithms for automatically determining COIs. We examine the effect of the structure and transience of COIs with the algorithms and validate algorithms using user-labeled data. Our analysis shows that the proposed algorithms correctly identify email as being sent from the human-identified COI with high accuracy. The structure and characteristics of COIs are explored analytically and broader conclusions about email use are posited. 1