Ground: A Data Context Service

Summary: Ground is an open-source data-context service that centralizes metadata about Applications, Behavior, and Change to capture and expose the information that informs data use. Paper presents the ABCs framing, a common metamodel and API, analysis of storage backends, and research directions for scalable, evolvable context management. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: h5dde690e3ae203d2
Venue: CIDR
Year: 2017
Pagerank: 9.4415148e-05
Overall Rank: 1,880 | 87.37%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Joseph M. Hellerstein (Trifacta; University of California Berkeley)
2. Vikram Sreekanti (University of California Berkeley)
3. Joseph E. Gonzalez (University of California Berkeley)
4. James Dalton (Capital One)
5. Akon Dey (Awake Networks)
6. Sreyashi Nag (University of Delhi)
7. Krishna Ramachandran (Skyhigh Networks)
8. Sudhanshu Arora (Cloudera)
9. Arka Bhattacharyya (University of California Berkeley)
10. Shirshanka Das (LinkedIn)
11. Mark Donsky (Cloudera)
12. Gabe Fierro (University of California Berkeley)
13. Chang She (Cloudera)
14. Carl Steinbach (LinkedIn)
15. Venkat Subramanian (Dataguise)
16. Eric Sun (LinkedIn)

BibTeX Citation

@inproceedings{hellerstein_cidr17,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '17},
        title = {{Ground: A Data Context Service}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Hellerstein, Joseph M. and Sreekanti, Vikram and Gonzalez, Joseph E. and Dalton, James and Dey, Akon and Nag, Sreyashi and Ramachandran, Krishna and Arora, Sudhanshu and Bhattacharyya, Arka and Das, Shirshanka and Donsky, Mark and Fierro, Gabe and She, Chang and Steinbach, Carl and Subramanian, Venkat and Sun, Eric},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 18 of 18 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
624	Data Lake Management: Challenges and Opportunities	2019	VLDB	0.00015479826
1,308	Automating Large-Scale Data Quality Verification	2018	VLDB	0.0001107886
2,187	Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities	2021	SIGMOD	8.8896655e-05
2,926	Organizing Data Lakes for Navigation	2020	SIGMOD	7.8442582e-05
3,670	Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?	2018	VLDB	7.1108704e-05
3,925	Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System	2025	SIGMOD	6.9214086e-05
5,031	Mining Precision Interfaces From Query Logs	2019	SIGMOD	6.3055788e-05
5,792	Optimizing Machine Learning Workloads in Collaborative Environments	2020	SIGMOD	5.99349e-05
6,652	Hindsight Logging for Model Training	2021	VLDB	5.7202005e-05
7,208	Dataset Relationship Management	2019	CIDR	5.584745e-05
7,582	Unity Catalog: Open and Universal Governance for the Lakehouse and Beyond	2025	SIGMOD	5.4910839e-05
8,402	Crossing the finish line faster when paddling the Data Lake with KAYAK	2017	VLDB	5.3391207e-05
8,583	OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance From Database Query Event Logs	2023	VLDB	5.3092552e-05
9,294	DataDiff: User-Interpretable Data Transformation Summaries for Collaborative Data Analysis	2018	SIGMOD	5.1995938e-05
9,417	Towards Observability for Production Machine Learning Pipelines	2022	VLDB	5.1803615e-05
9,996	Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle	2025	CIDR	5.0979044e-05
12,162	Peering through the Dark: An Owl's View of Inter-job Dependencies and Jobs' Impact in Shared Clusters	2019	SIGMOD	4.9793485e-05
12,185	Integration of Large-Scale Data Processing Systems and Traditional Parallel Database Technology	2019	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
89	WebTables: Exploring the Power of Tables on the Web	2008	VLDB	0.00035129658
509	Goods: Organizing Google's Datasets	2016	SIGMOD	0.00017071087
1,086	DataHub: Collaborative Data Science & Dataset Version Management at Scale	2015	CIDR	0.0001210322
1,502	VisTrails: Visualization meets Data Management	2006	SIGMOD	0.00010456416
2,280	Decibel: The Relational Dataset Branching System	2016	VLDB	8.7038926e-05
2,351	Schema Extraction for Tabular Data on the Web	2013	VLDB	8.5928057e-05
4,302	The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox	2015	CIDR	6.6778695e-05

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