The need for pattern discovery in long time-series data led researchers to develop interactive visualization tools and analytical algorithms for gaining insight into the data. Most of the literature on time-series data visualization either focus on a small number of tasks or a specific domain. We propose KronoMiner, a tool that embeds new interaction and visualization techniques as well as analytical capabilities for the visual exploration of time-series data.


Jian Zhao, Fanny Chevalier and Ravin Balakrishnan. Kronominer: Using Multi-Foci Navigation for the Visual Exploration of Time-Series Data In proceedings of the SIGCHI conference on Human Factors in computing systems (CHI'11), pp 1737-1746, May 2011. [Link to the ACM DL entry]

See also the related work Chronolenses.



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This research has been partly supported by the Centre for Information Visualization and Data-Driven Design (CIV-DDD).