Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System
By Tarique Siddiqui, Albert Kim, John Lee, K. G. Karahalios, Aditya G. Parameswaran. 2016
164 citations recorded by the source corpus.
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 that we describe in this paper. 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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Radwa El Shawi
- Jeffrey Heer
- Yeounoh Chung
- Sherif Sakr
- Mohamed Maher
- Felix Ouk
- Ferdinand Kossmann
- Bill Howe
In Computer Vision and Pattern Recognition
- Jeffrey Heer
- Dominik Moritz
- Bill Howe
- Kanit Wongsuphasawat
- Jock D. Mackinlay
- Anushka Anand
- Felix Ouk
- Zening Qu
In Signal Processing
- Kanit Wongsuphasawat
- Dominik Moritz
- Bill Howe
- Eli Upfal
- Tim Kraska
- Jock D. Mackinlay
- Sherif Sakr
- Jeffrey Heer
Citations in the corpus
- Automated Machine Learning: State-of-The-Art and Open Challenges (2019, positive): “zenVisage [110] extends the prior system seeDB.”
- Democratizing Data Science through Interactive Curation of ML Pipelines (2019, background): “On one hand it requires to move away from Python-like scripting languages, SQL and batch processing to visual and interactive environments [10, 15, 21, 26, 33, 43].”
- Voyager 2: Augmenting Visual Analysis with Partial View Specifications (2017, background): “Zenvisage [28] also presents a chart gallery based on queried visual patterns.”