Regularized Pairwise Relationship based Analytics for Structured Data

Summary: ATT-Reg: field-adaptive attentive regularization for pairwise attribute interactions in structured data. Bayesian-inspired adaptive regularization sets pairwise strengths from data and dependencies, boosting accuracy with interpretable feature attribution. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6647
Venue: SIGMOD
Year: 2023
Pagerank: 5.093636e-05
Overall Rank: 11,391 | 21.85%
DOI: 10.1145/3588936

Incoming Non-self Citations Over Time

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

Authors

1. Zhaojing Luo (National University of Singapore)
2. Shaofeng Cai (National University of Singapore)
3. Yatong Wang (University of Electronic Science and Technology of China)
4. Beng Chin Ooi (National University of Singapore)

BibTeX Citation

@inproceedings{luo_sigmod23,
        title = {{Regularized Pairwise Relationship based Analytics for Structured Data}},
        author = {Luo, Zhaojing and Cai, Shaofeng and Wang, Yatong and Ooi, Beng Chin},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588936},
        url = {https://dl.acm.org/doi/10.1145/3588936},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,814	METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection	2024	VLDB	6.4955135e-05
6,421	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB	5.8819368e-05
8,456	Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs	2024	VLDB	5.4217837e-05
10,390	AixelNet: A Pre-trained Model with Table-aware Adaptation for Structured Data Prediction	2026	SIGMOD	5.093636e-05
10,405	AixelAsk: A Stepwise-Guided Retrieval and Reasoning Framework for Large Table QA	2026	SIGMOD	5.093636e-05
10,856	Optimized Batch Prompting for Cost-effective LLMs	2025	VLDB	5.093636e-05
11,209	Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems	2024	VLDB	5.093636e-05
11,465	MINT: Detecting Fraudulent Behaviors from Time-series Relational Data	2023	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
112	HoloClean: Holistic Data Repairs with Probabilistic Inference	2017	VLDB	0.00032801121
334	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00020875082
498	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00017440583
729	Functional Dependency Discovery: An Experimental Evaluation of Seven Algorithms	2015	VLDB	0.00014539421
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
1,235	Towards Linear Algebra over Normalized Data	2017	VLDB	0.00011548457
1,279	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00011361878
2,179	Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra	2019	SIGMOD	9.0146333e-05
3,213	Estimating Cardinalities with Deep Sketches	2019	SIGMOD	7.6328677e-05
4,390	ARM-Net: Adaptive Relation Modeling Network for Structured Data	2021	SIGMOD	6.7290926e-05
4,609	F-IVM: Learning over Fast-Evolving Relational Data	2020	SIGMOD	6.6081313e-05
11,789	TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications	2020	SIGMOD	5.093636e-05

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