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Exploring time diaries using semi-automated activity pattern extraction
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
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.
2009 (English)In: electronic International Journal of Time Use Research (eIJTUR), Vol. 6, no 1, 1-25 p.Article in journal (Refereed) Published
Abstract [en]

Identifying patterns of activities in time diaries in order to understand the variety of daily life in terms of combinationsof activities performed by individuals in different groups is of interest in time use research. So far, activitypatterns have mostly been identified by visually inspecting representations of activity data or by using sequencecomparison methods, such as sequence alignment, in order to cluster similar data and then extract representativepatterns from these clusters. Both these methods are sensitive to data size, pure visual methods becometoo cluttered and sequence comparison methods become too time consuming. Furthermore, the patterns identifiedby both methods represent mostly general trends of activity in a population, while detail and unexpectedfeatures hidden in the data are often never revealed. We have implemented an algorithm that searches the timediaries and automatically extracts all activity patterns meeting user-defined criteria of what constitutes a validpattern of interest for the user’s research question. Amongst the many criteria which can be applied are a timewindow containing the pattern, minimum and maximum occurrences of the pattern, and number of people thatperform it. The extracted activity patterns can then be interactively filtered, visualized and analyzed to revealinteresting insights. Exploration of the results of each pattern search may result in new hypotheses which can besubsequently explored by altering the search criteria. To demonstrate the value of the presented approach weconsider and discuss sequential activity patterns at a population level, from a single day perspective.

Place, publisher, year, edition, pages
2009. Vol. 6, no 1, 1-25 p.
Keyword [en]
Time-geography, diaries, everyday life, activity patterns, visualization, data mining, sequential pattern mining
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-51231OAI: diva2:273589
Available from: 2009-10-22 Created: 2009-10-22 Last updated: 2015-03-20
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.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1331
Event-based data, activity diary data, event-sequences, interactive exploration, sequence identification, visual data mining
National Category
Computer Science
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)
Available from: 2010-09-01 Created: 2010-08-10 Last updated: 2015-09-22Bibliographically approved

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