Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/72835
Title: Behaviour mining for personalised desktop tool-support
Authors: Abela, Charlie (2016)
Keywords: Computer vision
User interfaces (Computer systems)
Algorithms
Issue Date: 2016
Citation: Abela, C. (2016). Behaviour mining for personalised desktop tool-support (Doctoral dissertation).
Abstract: It is natural for individuals to think about their work in terms of tasks, where a task could be the documents that they are working on or with, and/or the directories containing documents related to the same task. But directories do not necessarily contain all of the related documents, because documents might be emails and web pages, which are harder to place into the same directory. Furthermore, individuals are multitaskers and tasks get interrupted. This results in task-switching and the eventual resumption of some task (possibly the same one). Although different applications provide tools to support individuals to manage their information space, these tools fall short in supporting individuals when they need to keep and re-find documents that are part of their task or when a task-switch occurs. Task-keeping and re-finding (and/or resuming) with the current tool-support philosophy is both time-consuming and limited since it does not reflect the task concept. In contrast to a myriad of other approaches, this thesis considers task-keeping as a process that keeps track of changing tasks by identifying which documents belong to which tasks and when a task-switch occurs. An attempt is made to automatically identify the documents that belong to a task by solely relying on the user’s switching and re-visitation behaviour. In this way the process aims to reduce the keeping time without introducing new interruptions. Prior to addressing the problem, an experiment is conducted in a controlled environment with 22 participants to collect ground-truth task-related data. This dataset is later used to evaluate the task-keeping solution. It is extensively analysed from different perspectives using a visual-analytics tool that we built (called PiMx), to draw up the design requirements for the incremental density-based graph-clustering algorithm iDeTaCt. The algorithm is evaluated and extended and a marked improvement in its performance registered. A usability study is also performed with 15 participants over a five-day period using a prototype task-re-finding and resumption tool called PiMxT that uses iDeTaCt to automatically keep tasks. Both qualitative and quantitative feedback are solicited and analysed to verify the usefulness of the approach. The results show that the time to re-find and resume tasks with PiMxT is reduced by almost a half in the majority of the cases considered and that there is considerable potential behind this combined approach to task-keeping, re-finding and resumption.
Description: PH.D.ARTIFICIAL INTELLIGENCE
URI: https://www.um.edu.mt/library/oar/handle/123456789/72835
Appears in Collections:Dissertations - FacICT - 2016
Dissertations - FacICTAI - 2016

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Behaviour_mining_for_personalised_desktop_tool-support.pdf
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Behaviour Mining Survey_June_2013.docx
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Personal Web Task Keeping and Refinding_July_2015.xlsx
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Personal Web Task-Keeping and Refinding_July_2015.docx
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PiMxT Questionnaire_August_2015.pdf
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PiMxT Questionnaire_August_2015.xlsx
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27.15 kBMicrosoft Excel XMLView/Open Request a copy


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