Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System

TL;DR

zenvisage is a visual analytics system that simplifies data exploration by allowing users to specify and find interesting visual patterns and trends using an expressive query language, supported by an intuitive interface.

Contribution

The paper introduces ZQL, a novel visual query language for pattern discovery, and demonstrates its expressiveness and usability within the zenvisage platform.

Findings

ZQL is as expressive as the formal exploration algebra.

Users can effectively specify complex visual patterns with ZQL.

Preliminary experiments show promising performance and usability.

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 - 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.