MyEvents: A Personal Visual Analytics Approach for Mining Key Events and Knowledge Discovery in Support of Personal Reminiscence

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    Reminiscence is an important aspect in our life. It preserves precious memories, allows us to form our own identities and encourages us to accept the past. Our work takes the advantage of modern sensor technologies to support reminiscence, enabling self-monitoring of personal activities and individual movement in space and time on a daily basis. This paper presents MyEvents, a web-based personal visual analytics platform designed for non-computing experts, that allows for the collection of long-term location and movement data and the generation of event mementos. Our research is focused on two prominent goals in event reminiscence: (1) selection subjectivity and human involvement in the process of self-knowledge discovery and memento creation; and (2) the enhancement of event familiarity by presenting target events and their related information for optimal memory recall and reminiscence. A novel multi-significance event ranking model is proposed to determine significant events in the personal history according to user preferences for event category, frequency and regularity. The evaluation results show that MyEvents effectively fulfils the reminiscence goals and tasks.

    Documents

    • MyEvents: A Personal Visual Analytics Approach for Mining Key Events and Knowledge Discovery in Support of Personal Reminiscence

      Rights statement: © 2019 The Authors Computer Graphics Forum © 2019 The Eurographics Association and John Wiley & Sons Ltd. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.

      Accepted author manuscript, 5 MB, PDF-document

      Embargo ends: 05/01/2020

    DOI

    Original languageEnglish
    Number of pages16
    JournalComputer Graphics Forum
    Journal publication date05 Jan 2019
    Early online date05 Jan 2019
    DOIs
    Publication statusEarly online date - 05 Jan 2019

      Research areas

    • data mining, human–computer interfaces, interaction, methods and applications, visual analytics, visualization

    ID: 164165549