Semantic Scholar Open Access 2020 139 sitasi

Finding Related Tables in Data Lakes for Interactive Data Science

Yi Zhang Z. Ives

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Medicine Computer Science

Penulis (2)

Yi Zhang

Z. Ives

Format Sitasi

APA MLA BibTeX

Zhang, Y., Ives, Z. (2020). Finding Related Tables in Data Lakes for Interactive Data Science. https://doi.org/10.1145/3318464.3389726

Akses Cepat

Lihat di Sumber doi.org/10.1145/3318464.3389726

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 139×
Sumber Database: Semantic Scholar
DOI: 10.1145/3318464.3389726
Akses: Open Access ✓