zenvisage: Effortless Visual Data Exploration.
Year: 2016
Type: preprint
Abstract: 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 - zenvisage draws from use-cases in a variety of domains, including biology, mechanical engineering, commerce, and server monitoring. While analysts are free to use ZQL directly, we also expose zenvisage via a visual specification interface, which we also 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, as well as a user study to evaluate whether data scientists are able to effectively use zenvisage for real applications.
Source: arXiv (Cornell University)
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2337337754