Cloudlines: Compact display of event episodes in multiple time-series

Miloš Krstajić, Enrico Bertini, Daniel A. Keim

    Research output: Contribution to journalArticlepeer-review


    We propose incremental logarithmic time-series technique as a way to deal with time-based representations of large and dynamic event data sets in limited space. Modern data visualization problems in the domains of news analysis, network security and financial applications, require visual analysis of incremental data, which poses specific challenges that are normally not solved by static visualizations. The incremental nature of the data implies that visualizations have to necessarily change their content and still provide comprehensible representations. In particular, in this paper we deal with the need to keep an eye on recent events together with providing a context on the past and to make relevant patterns accessible at any scale. Our technique adapts to the incoming data by taking care of the rate at which data items occur and by using a decay function to let the items fade away according to their relevance. Since access to details is also important, we also provide a novel distortion magnifying lens technique which takes into account the distortions introduced by the logarithmic time scale to augment readability in selected areas of interest. We demonstrate the validity of our techniques by applying them on incremental data coming from online news streams in different time frames.

    Original languageEnglish (US)
    Article number6065010
    Pages (from-to)2432-2439
    Number of pages8
    JournalIEEE Transactions on Visualization and Computer Graphics
    Issue number12
    StatePublished - 2011


    • Incremental visualization
    • event based data
    • lens distortion

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Computer Vision and Pattern Recognition
    • Computer Graphics and Computer-Aided Design


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