Effortless data exploration with zenvisage: an expressive and interactive visual analytics system

"Data visualization is by far the most commonly used mechanism to explore data, especially by novice data analysts and data scientists. And yet, current visual analytics tools are rather limited in their ability to guide data scientists to interesting or desired visualizations: the process of visual data exploration remains cumbersome and time-consuming. We propose zenvisage, a platform for effortlessly visualizing interesting patterns, trends, or insights from large datasets. We describe zenvisage's general purpose visual query language, ZQL (""zee-quel"") for specifying the desired visual trend, pattern, or insight — ZQL draws from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual exploration algebra, and demonstrate that ZQL is at least as expressive as that algebra. While analysts are free to use ZQL directly, we also expose ZQL via a visual specification interface. We then describe our architecture and optimizations, preliminary experiments in supporting and optimizing for ZQL queries in our initial zenvisage prototype, and a user study to evaluate whether data scientists are able to effectively use zenvisage for real applications."

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2016

Author dc:creator

• Siddiqui, Tarique Ashraf

Contributors dc:contributor

• Parameswaran, Aditya G.
• Han, Jiawei

• Visual analytics
• Databases
• Query language
• Visualization

Statement dc:rights

• Copyright 2016 Tarique Ashraf Siddiqui

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/92826

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/92826