InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks
Yanna Lin, Haotian Li, Leni Yang, Aoyu Wu, Huamin Qu
摘要
This figure illustrates the process of using InkSight to document findings in a computational notebook. (A) displays the chart created by the user for data analysis. (B) demonstrates that when the user sketches atop the chart to identify areas of interest, InkSight automatically generates corresponding documentation on the right. (C) reveals how users can add more sketches and refine the documentations by performing interactions such as deleting, ordering, grouping, and editing. Abstract-Computational notebooks have become increasingly popular for exploratory data analysis due to their ability to support data exploration and explanation within a single document. Effective documentation for explaining chart findings during the exploration process is essential as it helps recall and share data analysis. However, documenting chart findings remains a challenge due to its time-consuming and tedious nature. While existing automatic methods alleviate some of the burden on users, they often fail to cater to users' specific interests. In response to these limitations, we present InkSight, a mixed-initiative computational notebook plugin that generates finding documentation based on the user's intent. InkSight allows users to express their intent in specific data subsets through sketching atop visualizations intuitively. To facilitate this, we designed two types of sketches, i.e., open-path and closed-path sketch. Upon receiving a user's sketch, InkSight identifies the sketch type and corresponding selected data items. Subsequently, it filters data fact types based on the sketch and selected data items before employing existing automatic data fact recommendation algorithms to infer data facts. Using large language models (GPT-3.5), InkSight converts data facts into effective natural language documentation. Users can conveniently fine-tune the generated documentation within InkSight. A user study with 12 participants demonstrated the usability and effectiveness of InkSight in expressing user intent and facilitating chart finding documentation.
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引用它的顶会 Paper12
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它引用的顶会 Paper19
- NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language QueriesArpit Narechania, Arjun Srinivasan, John T. StaskoIEEE VIS 2020 · 被引用 210 次
- TaleBrush: Sketching Stories with Generative Pretrained Language ModelsJohn Joon Young Chung, Wooseok Kim, Kang Min Yoo, Hwaran Lee 等CHI 2022 · 被引用 202 次
- Calliope: Automatic Visual Data Story Generation from a SpreadsheetDanqing Shi, Xinyue Xu, Fuling Sun, Yang Shi 等IEEE VIS 2020 · 被引用 179 次
- What's Wrong with Computational Notebooks? Pain Points, Needs, and Design OpportunitiesSouti Chattopadhyay, Ishita Prasad, Austin Z. Henley, Anita Sarma 等CHI 2020 · 被引用 162 次
- A Design Space for Applying the Freytag's Pyramid Structure to Data StoriesLeni Yang, Xian Xu, Xingyu Lan, Ziyan Liu 等IEEE VIS 2021 · 被引用 77 次
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