Effortless data exploration with zenvisage

Author:

Siddiqui Tarique1,Kim Albert2,Lee John1,Karahalios Karrie3,Parameswaran Aditya1

Affiliation:

1. University of Illinois, Urbana-Champaign (UIUC)

2. MIT

3. University of Illinois, Urbana-Champaign (UIUC) and Adobe Research

Abstract

Data visualization is by far the most commonly used mechanism to explore and extract insights from datasets, especially by novice data scientists. And yet, current visual analytics tools are rather limited in their ability to operate on collections of visualizations---by composing, filtering, comparing, and sorting them---to find those that depict desired trends or patterns. The process of visual data exploration remains a tedious process of trial-and-error. We propose zenvisage, a visual analytics platform for effortlessly finding desired visual patterns from large datasets. We introduce zenvisage's general purpose visual exploration language, ZQL ("zee-quel") for specifying the desired visual patterns, drawing 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---an algebra on collections of visualizations---and demonstrate that ZQL is as expressive as that algebra. zenvisage exposes an interactive front-end that supports the issuing of ZQL queries, and also supports interactions that are "short-cuts" to certain commonly used ZQL queries. To execute these queries, zenvisage uses a novel ZQL graph-based query optimizer that leverages a suite of optimizations tailored to the goal of processing collections of visualizations in certain pre-defined ways. Lastly, a user survey and study demonstrates that data scientists are able to effectively use zenvisage to eliminate error-prone and tedious exploration and directly identify desired visualizations.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

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1. Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. CoInsight: Visual Storytelling for Hierarchical Tables With Connected Insights;IEEE Transactions on Visualization and Computer Graphics;2024-06

3. Demonstration of FeVisQA: Free-Form Question Answering over Data Visualization;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

4. SlopeSeeker: A Search Tool for Exploring a Dataset of Quantifiable Trends;Proceedings of the 29th International Conference on Intelligent User Interfaces;2024-03-18

5. Optimizing Dataflow Systems for Scalable Interactive Visualization;Proceedings of the ACM on Management of Data;2024-03-12

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