ChronoCorrelator: Enriching Events with Time Series

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dc.contributor.authorvan Dortmont, Martijnen_US
dc.contributor.authorElzen, Stef van denen_US
dc.contributor.authorWijk, Jarke J. vanen_US
dc.contributor.editorGleicher, Michael and Viola, Ivan and Leitte, Heikeen_US
dc.date.accessioned2019-06-02T18:28:04Z
dc.date.available2019-06-02T18:28:04Z
dc.date.issued2019
dc.description.abstractEvent sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.en_US
dc.description.number3
dc.description.sectionheadersTime Series
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume38
dc.identifier.doi10.1111/cgf.13697
dc.identifier.issn1467-8659
dc.identifier.pages387-399
dc.identifier.urihttps://doi.org/10.1111/cgf.13697
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13697
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectInteraction design
dc.subjectMathematics of computing
dc.subjectTime series analysis
dc.titleChronoCorrelator: Enriching Events with Time Seriesen_US
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