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dc.contributor.authorBodesinsky, Peteren_US
dc.contributor.authorAlsallakh, Bilalen_US
dc.contributor.authorGschwandtner, Theresiaen_US
dc.contributor.authorMiksch, Silviaen_US
dc.contributor.editorE. Bertini and J. C. Robertsen_US
dc.date.accessioned2015-05-24T19:45:52Z
dc.date.available2015-05-24T19:45:52Z
dc.date.issued2015en_US
dc.identifier.urihttp://dx.doi.org/10.2312/eurova.20151106en_US
dc.description.abstractEvent data is generated in many domains, like business process management, industry or healthcare. These datasets are often unstructured, exhibit variant behaviour, and may contain errors. Before applying automated analysis methods, such as process mining algorithms, the analyst needs to understand the dependency between events in order to decide which analysis method might fit the recorded events. We define a categorization scheme of event dependencies and describe a preliminary approach for exploring event data, combining visual exploration with pattern mining. Events of interest can be selected, grouped, and visually explored, using either a sequential or a temporal scale. We present two use cases with shopping event data and report expert feedback on our approach.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectH.2.8 [Database Management]en_US
dc.subjectDatabase Applicationen_US
dc.subjectData Mining H.5.2 [Information Interfaces and Presentation]en_US
dc.subjectUser Interfacesen_US
dc.titleExploration and Assessment of Event Dataen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)en_US
dc.description.sectionheadersTime-series and Temporal Dataen_US
dc.identifier.doi10.2312/eurova.20151106en_US
dc.identifier.pages67-71en_US


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