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Seeing Beyond Statistics: Visual Exploration of Productivity on a Construction Site
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. (Norrköping Visualization and Interaction Studio (NVIS))ORCID iD: 0000-0003-4761-8601
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. (Norrköping Visualization and Interaction Studio (NVIS))ORCID iD: 0000-0002-9466-9826
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. (Norrköping Visualization and Interaction Studio (NVIS))
2008 (English)In: Vis 2008, Visualisation: Visualisation in Built and Rural Enviroments, Los Alamitos, CA, USA: IEEE Computer Society, 2008, 37-42 p.Conference paper, Published paper (Refereed)
Abstract [en]

Work on the construction site is known to be inefficient due to workers spending much time waiting for materials, transporting materials and from frequent interruptions of tasks. Studies on the construction site typically use statistical measures to analyse the sampled data about work and such measures, while very useful, can overlook important features of the data. In this paper we apply a previously developed approach, derived from Time Geographical methods, to visually represent the sampled construction productivity data and show that this method may enable the analyst to better understand the distribution of activities, and how they are interrelated and dependent upon each other. 

Place, publisher, year, edition, pages
Los Alamitos, CA, USA: IEEE Computer Society, 2008. 37-42 p.
Keyword [en]
construction productivity, visual exploration, visualization, work sampling
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-43044DOI: 10.1109/VIS.2008.27ISI: 000258052600006Local ID: 71159ISBN: 978-0-7695-3271-4 (print)OAI: oai:DiVA.org:liu-43044DiVA: diva2:263901
Conference
International Conference Visualisation (VIS 2008), London, UK, 9-11 July 2008
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2015-09-22Bibliographically approved
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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Vrotsou, KaterinaYnnerman, AndersCooper, Matthew D.

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