Qutaber: task-based exploratory data analysis with enriched context awareness

Jiang, Qi; Sun, Guodao; Li, Tong; Tang, Jingwei; Xia, Wang; Zhu, Sujia; Liang, Ronghua

doi:10.1007/s12650-024-00975-1

J Vis

2024

DOI: 10.1007/s12650-024-00975-1

|View full text |Cite

Qutaber: task-based exploratory data analysis with enriched context awareness

Qi Jiang,

Guodao Sun,

Tong Li

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

MAVIDSQL: A Model-Agnostic Visualization for Interpretation and Diagnosis of Text-to-SQL Tasks

Tang,

Sun,

Chen

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

MAVIDSQL: A Model-Agnostic Visualization for Interpretation and Diagnosis of Text-to-SQL Tasks

Tang,

Sun,

Chen

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

TaskFinder: A Semantics-Based Methodology for Visualization Task Recommendation

Coelho,

Ghai,

Krishna

et al. 2024

Analytics

View full text Add to dashboard Cite

Data visualization has entered the mainstream, and numerous visualization recommender systems have been proposed to assist visualization novices, as well as busy professionals, in selecting the most appropriate type of chart for their data. Given a dataset and a set of user-defined analytical tasks, these systems can make recommendations based on expert coded visualization design principles or empirical models. However, the need to identify the pertinent analytical tasks beforehand still exists and often requires domain expertise. In this work, we aim to automate this step with TaskFinder, a prototype system that leverages the information available in textual documents to understand domain-specific relations between attributes and tasks. TaskFinder employs word vectors as well as a custom dependency parser along with an expert-defined list of task keywords to extract and rank associations between tasks and attributes. It pairs these associations with a statistical analysis of the dataset to filter out tasks irrelevant given the data. TaskFinder ultimately produces a ranked list of attribute–task pairs. We show that the number of domain articles needed to converge to a recommendation consensus is bounded for our approach. We demonstrate our TaskFinder over multiple domains with varying article types and quantities.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Qutaber: task-based exploratory data analysis with enriched context awareness

Cited by 2 publications

References 45 publications

MAVIDSQL: A Model-Agnostic Visualization for Interpretation and Diagnosis of Text-to-SQL Tasks

MAVIDSQL: A Model-Agnostic Visualization for Interpretation and Diagnosis of Text-to-SQL Tasks

TaskFinder: A Semantics-Based Methodology for Visualization Task Recommendation

Contact Info

Product

Resources

About