CAFE: Constraint-Aware Feature Extraction from Large Databases

Summary: CAFE extracts features from large DBs while enforcing high-level constraints (consistency, interpretability, fairness) by mapping them to low-level pruning strategies and using an inverted index to find candidate columns. An optimizer-like planner uses sample-based estimates, models strategy dependencies, and orders pruning to maximize downstream ML accuracy while bounding runtime and feature quality. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 378
Venue: CIDR
Year: 2020
Pagerank: 4.1945683e-05
Overall Rank: 11,547 | 19.67%
DOI: -

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Mahdi Esmailoghli
2. Ziawasch Abedjan

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,769	Automated Feature Engineering for Algorithmic Fairness	2021	VLDB	5.934329e-05
6,270	MATE: Multi-Attribute Table Extraction	2022	VLDB	5.1337451e-05
10,836	Data Discovery in Data Lakes: Operations, Indexes, Systems	2025	VLDB	4.1945683e-05
11,476	Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study	2021	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
420	InfoGather: Entity Augmentation and Attribute Discovery By Holistic Matching with Web Tables	2012	SIGMOD	0.00023719065
1,277	The Data Civilizer System	2017	CIDR	0.00012879695

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