A System for Effortless Visual Exploration of Large Datasets.

Why Zenvisage?

In the "big data" era, everyone has access to massive quantities of data, and are struggling to make sense of, and derive value from such data. The state of the art for non-programmers is to load this data into a visualization tool, and repeatedly generate visualizations until desired ones are identified. This exploration is painful, tedious, and time-consuming, meaning that the "insight per unit time" is exceedingly low.
What if you could "skip ahead to the insights"?
Zenvisage is a visual exploration system that can automatically identify and recommend interesting visualizations. The user can specify at a high level what they are looking for, and the system will do the rest.

Key Features

Zenvisage enables users to effortlessly receive visualization recommendations for interesting trends, patterns, and insights from large datasets. Here are the key features of Zenvisage:
  • Interactive Insight Sketch Interface. Zenvisage users can directly draw the trend-line, bar chart, or scatterplot they are looking for, and then rely on the system to find appropriate matches: for instance, a person browsing a dataset of material properties may be looking for those materials displaying a specific correlation between two properties. Users can also drag-and-drop trends onto the canvas and then subsequently modify the trend. Using this interface, users can specify the insights they are looking for, and expect Zenvisage to find matches, much like a "visualization search engine".
  • Sophisticated Visual Query Interface. For more complex requests, Zenvisage supports a query language, called ZQL (pronounced "zee-quel"), short for Zenvisage Query Language, a flexible and intuitive mechanism to specify desired insights from visualizations. Using a small number of ZQL lines, users can explore trends, patterns, and insights in any which way they desire.
  • Visualization Recommendations. In addition to returning results for user-submitted queries, zenvisage runs a host of parallel queries to find the most interesting trends for that subset of data the user is currently viewing and presents them as visualization recommendations.
Zenvisage generalizes and extends our prior system SeeDB (see papers).

Current Partners

Zenvisage is being developed with close collaboration with a number of partners who have large datasets and a pressing need for automated data exploration. Here is a small sample:
  • Engineering Data Analysis. We are developing SEED (short for System for Electrolyte Exploration and Discovery), a version of Zenvisage tailored for electrolyte property exploration, with battery scientists at Carnegie Mellon University.
  • Genomic Data Analysis. We are developing Genvisage, a version of Zenvisage tailored to genomic data analysis, with bioinformatics researchers at the NIH KnowEnG center, established at Illinois and Mayo Clinic.
  • Environmental Data Analysis. We are working with environmental scientists to develop a version of Zenvisage tailored to tracking and detecting patterns in environmental sensor data.
  • Advertising Data Analysis. We are developing a version of Zenvisage tailored to advertising data analysis, with engineers at Turn, Inc.
  • Educational Data Analysis. We are tailoring Zenvisage to work on educational data to help administrators at Illinois understand student trends, comparisons, and patterns.

Recent Releases

  • Jan 1, 2017: Moving beyond private betas, we are ready with our first zenvisage public release! You can always follow along the progress of our development here.
  • Oct 15, 2016: Our full paper on zenvisage has been accepted at VLDB 2017. Pre-camera ready version here.
  • Oct 11, 2016: Our demo paper on zenvisage has been accepted at CIDR 2017. Pre-camera ready version here.
  • May 13, 2016: Slides from a short talk on here.
  • May 1, 2016: Release of a new preprint on zenvisage. Paper here.


Contact Us

Zenvisage is being developed by a team of undergraduate and graduate students headed by Prof. Aditya Parameswaran, along with a number of collaborators, including Prof. Karrie Karahalios and Prof. Samuel Madden. The list of contributors includes: Tarique Siddiqui, John Lee, Albert Kim, Edward Xue, Charaon Wang, Yuxuan Zou and Changfeng Liu. There are other students working on related projects: Himel Dev, Silu Huang, Stephen Macke, Sajjadur Rahman, Tana Wattanawaroon.
Please reach out to the lead PhD student, Tarique Siddiqui (tsiddiq2@illinois.edu) if you'd like to either contribute, or be a beta tester of Zenvisage!

    © Aditya Parameswaran 2016

With thanks to our seed funding sources: