Fast-forwarding to desired visualizations with zenvisage

Tarique Siddiqui, John Lee, Albert Kim, Edward Xue, Chaoran Wang, Yuxuan Zou, Lijin Guo, Changfeng Liu, Xiaofo Yu, Karrie Karahalios, Aditya Parameswaran

Research output: Contribution to conferencePaperpeer-review

Abstract

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.

Original languageEnglish (US)
StatePublished - 2017
Event8th Biennial Conference on Innovative Data Systems Research, CIDR 2017 - Santa Cruz, United States
Duration: Jan 8 2017Jan 11 2017

Conference

Conference8th Biennial Conference on Innovative Data Systems Research, CIDR 2017
CountryUnited States
CitySanta Cruz
Period1/8/171/11/17

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence
  • Information Systems and Management
  • Hardware and Architecture

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