Urania: Visualizing Data Analysis Pipelines for Natural Language-Based Data Exploration

TL;DR

Urania is an interactive system that visualizes data analysis pipelines generated from natural language questions, improving interpretability and exploration in data analysis.

Contribution

It introduces a novel data-aware question decomposition algorithm and visualizes analysis pipelines to enhance data exploration and understanding.

Findings

The decomposition algorithm outperforms state-of-the-art in accuracy.

Urania improves dataset exploration effectiveness.

Expert feedback supports system usability and interpretability.

Abstract

Exploratory Data Analysis (EDA) is an essential yet tedious process for examining a new dataset. To facilitate it, natural language interfaces (NLIs) can help people intuitively explore the dataset via data-oriented questions. However, existing NLIs primarily focus on providing accurate answers to questions, with few offering explanations or presentations of the data analysis pipeline used to uncover the answer. Such presentations are crucial for EDA as they enhance the interpretability and reliability of the answer, while also helping users understand the analysis process and derive insights. To fill this gap, we introduce Urania, a natural language interactive system that is able to visualize the data analysis pipelines used to resolve input questions. It integrates a natural language interface that allows users to explore data via questions, and a novel data-aware question decomposition algorithm that resolves each input question into a data analysis pipeline. This pipeline is visualized in the form of a datamation, with animated presentations of analysis operations and their corresponding data changes. Through two quantitative experiments and expert interviews, we demonstrated that our data-aware question decomposition algorithm outperforms the state-of-the-art technique in terms of execution accuracy, and that Urania can help people explore datasets better. In the end, we discuss the observations from the studies and the potential future works.