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Capturing patterns of everyday life: presentation of the visualization method VISUAL-TimePAcTS
Linköping University, The Tema Institute, Technology and Social Change. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0003-4133-1204
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.ORCID iD: 0000-0003-4761-8601
2006 (English)In: International Association for Time Use Research Annual Conference, Copenhagen, Denmark: Danish National Institute of Social Research , 2006Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Copenhagen, Denmark: Danish National Institute of Social Research , 2006.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-35802Local ID: 28606OAI: oai:DiVA.org:liu-35802DiVA: diva2:256650
Conference
International Association for Time-Use Research Conference, Copenhagen, Denmark
Note
CD proceedings hence no page numbers.Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2015-06-02
In thesis
1. Everyday mining: Exploring sequences in event-based data
Open this publication in new window or tab >>Everyday mining: Exploring sequences in event-based data
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Utforskning av sekvenser i händelsebaserade data
Abstract [en]

Event-based data are encountered daily in many disciplines and are used for various purposes. They are collections of ordered sequences of events where each event has a start time and a duration. Examples of such data include medical records, internet surfing records, transaction records, industrial process or system control records, and activity diary data.

This thesis is concerned with the exploration of event-based data, and in particular the identification and analysis of sequences within them. Sequences are interesting in this context since they enable the understanding of the evolving character of event data records over time. They can reveal trends, relationships and similarities across the data, allow for comparisons to be made within and between the records, and can also help predict forthcoming events.The presented work has researched methods for identifying and exploring such event-sequences which are based on modern visualization, interaction and data mining techniques.

An interactive visualization environment that facilitates analysis and exploration of event-based data has been designed and developed, which permits a user to freely explore different aspects of this data and visually identify interesting features and trends. Visual data mining methods have been developed within this environment, that facilitate the automatic identification and exploration of interesting sequences as patterns. The first method makes use of a sequence mining algorithm that identifies sequences of events as patterns, in an iterative fashion, according to certain user-defined constraints. The resulting patterns can then be displayed and interactively explored by the user.The second method has been inspired by web-mining algorithms and the use of graph similarity. A tree-inspired visual exploration environment has been developed that allows a user to systematically and interactively explore interesting event-sequences.Having identified interesting sequences as patterns it becomes interesting to further explore how these are incorporated across the data and classify the records based on the similarities in the way these sequences are manifested within them. In the final method developed in this work, a set of similarity metrics has been identified for characterizing event-sequences, which are then used within a clustering algorithm in order to find similarly behavinggroups. The resulting clusters, as well as attributes of the clusteringparameters and data records, are displayed in a set of linked views allowing the user to interactively explore relationships within these.

The research has been focused on the exploration of activity diary data for the study of individuals' time-use and has resulted in a powerful research tool facilitating understanding and thorough analysis of the complexity of everyday life.

Place, publisher, year, edition, pages
Norrköping: Linköping University Electronic Press, 2010. 76 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1331
Keyword
Event-based data, activity diary data, event-sequences, interactive exploration, sequence identification, visual data mining
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-58311 (URN)978-91-7393-343-8 (ISBN)
Public defence
2010-09-10, Domteater, Norrköpings Visualiseringscenter C, Kungsgatan 54, 602 33 Norrköping, 09:15 (English)
Opponent
Supervisors
Available from: 2010-09-01 Created: 2010-08-10 Last updated: 2015-09-22Bibliographically approved

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Ellegård, KajsaVrotsou, Katerina

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