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ActiviTree: Interactive Visual Exploration of Sequences in Event-Based Data Using Graph Similarity
Linköpings universitet, Institutionen för teknik och naturvetenskap, Visuell informationsteknologi och applikationer. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0003-4761-8601
Linköpings universitet, Institutionen för teknik och naturvetenskap, Visuell informationsteknologi och applikationer. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för teknik och naturvetenskap, Visuell informationsteknologi och applikationer. Linköpings universitet, Tekniska högskolan.
2009 (engelsk)Inngår i: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, ISSN 1077-2626, Vol. 15, nr 6, s. 945-952Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The identification of significant sequences in large and complex event-based temporal data is a challenging problem with applications in many areas of todays information intensive society. Pure visual representations can be used for the analysis, but are constrained to small data sets. Algorithmic search mechanisms used for larger data sets become expensive as the data size increases and typically focus on frequency of occurrence to reduce the computational complexity, often overlooking important infrequent sequences and outliers. In this paper we introduce an interactive visual data mining approach based on an adaptation of techniques developed for web searching, combined with an intuitive visual interface, to facilitate user-centred exploration of the data and identification of sequences significant to that user. The search algorithm used in the exploration executes in negligible time, even for large data, and so no pre-processing of the selected data is required, making this a completely interactive experience for the user. Our particular application area is social science diary data but the technique is applicable across many other disciplines.

sted, utgiver, år, opplag, sider
2009. Vol. 15, nr 6, s. 945-952
Emneord [en]
Interactive visual exploration, event-based data, sequence identification, graph similarity, node similarity
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-51476DOI: 10.1109/TVCG.2009.117OAI: oai:DiVA.org:liu-51476DiVA, id: diva2:275286
Tilgjengelig fra: 2009-11-04 Laget: 2009-11-04 Sist oppdatert: 2017-12-12
Inngår i avhandling
1. Everyday mining: Exploring sequences in event-based data
Åpne denne publikasjonen i ny fane eller vindu >>Everyday mining: Exploring sequences in event-based data
2010 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[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.

sted, utgiver, år, opplag, sider
Norrköping: Linköping University Electronic Press, 2010. s. 76
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1331
Emneord
Event-based data, activity diary data, event-sequences, interactive exploration, sequence identification, visual data mining
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-58311 (URN)978-91-7393-343-8 (ISBN)
Disputas
2010-09-10, Domteater, Norrköpings Visualiseringscenter C, Kungsgatan 54, 602 33 Norrköping, 09:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2010-09-01 Laget: 2010-08-10 Sist oppdatert: 2020-02-19bibliografisk kontrollert

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