TY - CONF
T1 - Fast-forwarding to desired visualizations with zenvisage
AU - Siddiqui, Tarique
AU - Lee, John
AU - Kim, Albert
AU - Xue, Edward
AU - Wang, Chaoran
AU - Zou, Yuxuan
AU - Guo, Lijin
AU - Liu, Changfeng
AU - Yu, Xiaofo
AU - Karahalios, Karrie
AU - Parameswaran, Aditya
N1 - Funding Information:
We thank the anonymous reviewers for their valuable feedback. We acknowledge support from grant IIS-1513407 and IIS-1633755 awarded by the National Science Foundation, grant 1U54GM114838 awarded by NIGMS and 3U54EB020406-02S1 awarded by NIBIB through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov), and funds from Adobe, Google, and the Siebel Energy Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies and organizations.
PY - 2017
Y1 - 2017
N2 - Data exploration and analysis, especially for non-programmers, remains a tedious and frustrating process of trial-and-error—data scientists spend many hours poring through visualizations in the hope of finding those that match desired patterns. We demonstrate zenvisage, an interactive data exploration system tailored towards “fast-forwarding” to desired trends, patterns, or insights, without much effort from the user. zenvisage’s interface supports simple drag- and-drop and sketch-based interactions as specification mechanisms for the exploration need, as well as an intuitive data exploration language called ZQL for more complex needs. zenvisage is being developed in collaboration with ad analysts, battery scientists, and genomic data analysts, and will be demonstrated on similar datasets.
AB - Data exploration and analysis, especially for non-programmers, remains a tedious and frustrating process of trial-and-error—data scientists spend many hours poring through visualizations in the hope of finding those that match desired patterns. We demonstrate zenvisage, an interactive data exploration system tailored towards “fast-forwarding” to desired trends, patterns, or insights, without much effort from the user. zenvisage’s interface supports simple drag- and-drop and sketch-based interactions as specification mechanisms for the exploration need, as well as an intuitive data exploration language called ZQL for more complex needs. zenvisage is being developed in collaboration with ad analysts, battery scientists, and genomic data analysts, and will be demonstrated on similar datasets.
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M3 - Paper
AN - SCOPUS:85084013313
T2 - 8th Biennial Conference on Innovative Data Systems Research, CIDR 2017
Y2 - 8 January 2017 through 11 January 2017
ER -