StructVizor: Interactive Profiling of Semi-Structured Textual Data

要旨

Data profiling plays a critical role in understanding the structure of complex datasets and supporting numerous downstream tasks, such as social media analytics and financial fraud detection. While existing research predominantly focuses on structured data formats, a substantial portion of semi-structured textual data still requires ad-hoc and arduous manual profiling to extract and comprehend its internal structures. In this work, we propose StructVizor, an interactive profiling system that facilitates sensemaking and transformation of semi-structured textual data. Our tool mainly addresses two challenges: a) extracting and visualizing the diverse structural patterns within data, such as how information is organized or related, and b) enabling users to efficiently perform various wrangling operations on textual data. Through automatic data parsing and structure mining, StructVizor enables visual analytics of structural patterns, while incorporating novel interactions to enable profile-based data wrangling. A comparative user study involving 12 participants demonstrates the system's usability and its effectiveness in supporting exploratory data analysis and transformation tasks.

著者
Yanwei Huang
Zhejiang University, Hangzhou, Zhejiang, China
Yan Miao
Zhejiang University, Hangzhou, Zhejiang, China
Di Weng
Zhejiang University, Ningbo, Zhejiang, China
Adam Perer
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Yingcai Wu
Zhejiang University, Hangzhou, Zhejiang, China
DOI

10.1145/3706598.3713484

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713484

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Engaging with Data

Annex Hall F206
7 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
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