Chart Constellations: Effective Chart Summarization for Collaborative and Multi-User Analyses

Shenyu Xu, Chris Bryan, Jianping Kelvin Li, Jian Zhao, Kwan Liu Ma

Research output: Contribution to journalReview articlepeer-review

21 Scopus citations


Many data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart-driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta-visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high-level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.

Original languageEnglish (US)
Pages (from-to)75-86
Number of pages12
JournalComputer Graphics Forum
Issue number3
StatePublished - Jun 2018
Externally publishedYes

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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