Ground: A Data Context Service

By Joseph M Hellerstein, Vikram Sreekanti, Joseph E. Gonzalez, J. Dalton, Akon Dey, Sreyashi Nag, Krishna Ramachandran, S. Arora, A. Bhattacharyya, Shirshanka Das and 6 more. 2017

118 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Automated Machine Learning: State-of-The-Art and Open Challenges (2019, background): “Ground [47] is an open-source data context service that manages the data storage which facilitates the good use of data.”
Automating Large-Scale Data Quality Verification (2018, background): “…metadata of artifacts produced during machine learning workloads, including schemas and summary statistics of the datasets used for training and testing [47, 48, 40, 29, 28, 20, 33, 41], the discovery and organization of enterprise datasets [16], and machine learning speciﬁc san-ity checks [45].”
Data Lake Management: Challenges and Opportunities (2019, background): “In contrast, the Ground project collects the context of data which includes applications, behaviors, and changes of data and stores the metadata in queryable graph structures [23].”
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? (2017, background): “There is also a growing interest in making data discovery and other forms of metadata management easier [13, 18].”