Accelerating Data Exploration via Visual Query Systems


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. One proposed solution is to design visual query systems that allow scientists to search for desired patterns in their datasets. While many existing visual query systems promise to accelerate exploratory data analysis by facilitating this search, they are unfortunately not widely used in practice.

Our Approach

Through a year-long collaboration with scientists in astronomy, genetics, and material science, we study the impact of various features within visual query systems that can aid rapid visual data analysis, and how visual query systems fit into a scientists’ analysis workflow. As part of the design study, we developed Zenvisage, 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 search for the desired trends. Our design study findings also offer design guidelines for improving the usability and adoption of next-generation visual query systems, paving the way for visual query systems to be applied to a variety of scientific domains.



Doris Jung-Lin Lee, John Lee, Tarique Siddiqui, Jaewoo Kim, Karrie Karahalios, Aditya Parameswaran. Accelerating Scientific Data Exploration via Visual Query Systems. pdf

Tarique Siddiqui, Albert Kim, John Lee, Karrie Karahalios, Aditya Parameswaran. Effortless Visual Data Exploration with Zenvisage: An Interactive and Expressive Visual Analytics System. 43rd International Conference on Very Large Data Bases (VLDB), Munich, Germany. September 2017. pdf

Tarique Siddiqui, John Lee, Albert Kim, Edward Xue, Xiaofo Yu, Sean Zou, Lijin Guo, Changfeng Liu, Chaoran Wang, Karrie Karahalios, Aditya Parameswaran. Fast-Forwarding to Desired Visualizations with zenvisage. Conference on Innovative Database Research (CIDR), Chaminade, USA. January 2017. pdf


Doris Jung-Lin Lee, John Lee, Tarique Siddiqui