Brainwash: A Data System for Feature Engineering

Summary: Brainwash: a data-system vision to ease feature engineering for large ML-driven systems by shortening the Explore–Extract–Evaluate loop and revealing how feature code interacts with massive datasets. Focuses on faster iterative feedback and run reuse. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 212
Venue: CIDR
Year: 2013
Pagerank: 8.3524514e-05
Overall Rank: 2,604 | 82.14%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Michael Anderson (University of Michigan)
2. Dolan Antenucci (University of Michigan)
3. Victor Bittorf (University of Wisconsin)
4. Matthew Burgess (University of Michigan)
5. Michael Cafarella (University of Michigan)
6. Arun Kumar (University of Wisconsin)
7. Feng Niu (University of Wisconsin)
8. Yongjoo Park (University of Michigan)
9. Christopher Ré (University of Wisconsin)
10. Ce Zhang (University of Wisconsin)

BibTeX Citation

@inproceedings{anderson_cidr13,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '13},
        title = {{Brainwash: A Data System for Feature Engineering}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Anderson, Michael and Antenucci, Dolan and Bittorf, Victor and Burgess, Matthew and Cafarella, Michael and Kumar, Arun and Niu, Feng and Park, Yongjoo and Ré, Christopher and Zhang, Ce},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 23 of 23 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
446	Monkey: Optimal Navigable Key-Value Store	2017	SIGMOD	0.00018332392
715	Learning Generalized Linear Models Over Normalized Data	2015	SIGMOD	0.00014655327
764	To Join or Not to Join? Thinking Twice about Joins before Feature Selection	2016	SIGMOD	0.00014226652
1,250	Data Management in Machine Learning: Challenges, Techniques, and Systems	2017	SIGMOD	0.00011485301
1,569	HELIX: Holistic Optimization for Accelerating Iterative Machine Learning	2019	VLDB	0.00010335423
1,616	The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models	2018	SIGMOD	0.00010213691
3,681	Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?	2018	VLDB	7.2037388e-05
4,410	Extracting Databases from Dark Data with DeepDive	2016	SIGMOD	6.717496e-05
4,993	Key-Value Storage Engines	2020	SIGMOD	6.4096682e-05
5,297	An Integrated Development Environment for Faster Feature Engineering	2014	VLDB	6.2772486e-05
5,557	Demonstration of Santoku: Optimizing Machine Learning over Normalized Data	2015	VLDB	6.17499e-05
5,785	BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees	2019	SIGMOD	6.0892672e-05
5,804	A Relational Framework for Classifier Engineering	2017	PODS	6.0829486e-05
5,978	From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems	2019	SIGMOD	6.0212877e-05
7,985	Schema Independent Relational Learning	2017	SIGMOD	5.5125969e-05
8,979	Cerebro: A Layered Data Platform for Scalable Deep Learning	2021	CIDR	5.3399615e-05
9,585	Hephaestus: Data Reuse for Accelerating Scientific Discovery	2015	CIDR	5.252604e-05
10,466	InferF: Declarative Factorization of AI/ML Inferences over Joins	2026	SIGMOD	5.093636e-05
11,674	Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study	2021	SIGMOD	5.093636e-05
12,173	Which Concepts Are Worth Extracting?	2014	SIGMOD	5.093636e-05
12,217	The Case for Personal Data-Driven Decision Making	2014	VLDB	5.093636e-05
13,561	Faster Evaluation of Labor-Intensive Features	2015	CIDR	-
13,649	Ringtail: A Generalized Nowcasting System	2013	VLDB	-

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

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