Fast-Forwarding to Desired Visualizations with Zenvisage - Siddiqui, Tarique; Lee, John; Kim, Alber; xue, Edward; Wang, Chaoran; Zou, Yuxuan; Liu, Changfeng; Guo, Lijin; Yu, Xiaofo; Karahalios, Karrie; Parameswaran, Aditya (CIDR, 2017)
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.