Finding Related Tables in Data Lakes for Interactive Data Science
SIGMOD/PODS '20: International Conference on Management of Data Portland OR USA June, 2020(2020)
Abstract
Many modern data science applications build on data lakes, schema-agnostic repositories of data files and data products that offer limited organization and management capabilities. There is a need to build data lake search capabilities into data science environments, so scientists and analysts can find tables, schemas, workflows, and datasets useful to their task at hand. We develop search and management solutions for the Jupyter Notebook data science platform, to enable scientists to augment training data, find potential features to extract, clean data, and find joinable or linkable tables. Our core methods also generalize to other settings where computational tasks involve execution of programs or scripts.
MoreTranslated text
Key words
data lakes,interactive data science,notebooks,table search
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined