Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?

Summary: Revisits “safe” avoidance of key–foreign-key joins for decision trees, nonlinear SVMs, and ANNs. Extensive real-data and simulation results show these high-capacity models are more robust than linear classifiers, overturning prior intuition. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 11937
Venue: VLDB
Year: 2018
Pagerank: 7.2037388e-05
Overall Rank: 3,681 | 74.75%
DOI: 10.14778/3157794.3157804

Incoming Non-self Citations Over Time

Authors

1. Vraj Shah (University of California San Diego)
2. Arun Kumar (University of California San Diego)
3. Xiaojin Zhu (University of Wisconsin)

BibTeX Citation

@article{shah_vldb18,
        title = {{Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?}},
        author = {Shah, Vraj and Kumar, Arun and Zhu, Xiaojin},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {3},
        pages = {366--379},
        doi = {10.14778/3157794.3157804},
        url = {https://doi.org/10.14778/3157794.3157804},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,121	ARDA: Automatic Relational Data Augmentation for Machine Learning	2020	VLDB	0.00012093059
2,994	Data Acquisition for Improving Machine Learning Models	2021	VLDB	7.8762343e-05
3,589	Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins	2022	VLDB	7.2812353e-05
4,735	Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation	2022	SIGMOD	6.5315782e-05
4,966	Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and Beyond	2021	SIGMOD	6.4225454e-05
5,396	Putting Things into Context: Rich Explanations for Query Answers using Join Graphs	2021	SIGMOD	6.2329373e-05
7,112	Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning	2023	VLDB	5.6990782e-05
8,842	Data Acquisition for Improving Model Confidence	2024	SIGMOD	5.3589295e-05
11,262	Enriching Relations with Additional Attributes for ER	2024	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
106	The MADlib Analytics Library or MAD Skills, the SQL	2012	VLDB	0.00033539462
415	SystemML: Declarative Machine Learning on Spark	2016	VLDB	0.0001888524
427	Big Data Integration	2013	VLDB	0.00018661543
518	Towards a Unified Architecture for in-RDBMS Analytics	2012	SIGMOD	0.00017167492
532	MLbase: A Distributed Machine-learning System	2013	CIDR	0.00017072641
536	Learning Linear Regression Models over Factorized Joins	2016	SIGMOD	0.0001693369
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
835	Scaling Factorization Machines to Relational Data	2013	VLDB	0.00013721583
1,147	Data Management Challenges in Production Machine Learning	2017	SIGMOD	0.00011974846
1,902	Ground: A Data Context Service	2017	CIDR	9.506714e-05
2,604	Brainwash: A Data System for Feature Engineering	2013	CIDR	8.3524514e-05
5,557	Demonstration of Santoku: Optimizing Machine Learning over Normalized Data	2015	VLDB	6.17499e-05
7,403	Feature Selection in Enterprise Analytics: A Demonstration using an R-based Data Analytics System	2013	VLDB	5.6249895e-05

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