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 G
N1 - Funding Information:
The work was partly supported Grant Agency of Czech Republic project No. 102/08/0707, Czech Ministry of Education project No. MSM0021630528 and by BUT FIT grant No. FIT-10-S-2. F. Grézl was supported by Grant Agency of Czech Republic under project No. GP102/09/P635.
Publisher Copyright:
© 2017 Conference on Innovative Data Systems Research (CIDR). All rights reserved.
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 -