F-IVM: Learning over Fast-Evolving Relational Data
Summary: F-IVM enables real-time analytics over training datasets defined by queries on fast-evolving relational databases. Demonstrated on model selection, Chow-Liu trees, and ridge linear regression to support continuous ML workloads in rapidly changing data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Milos Nikolic
- 2. Haozhe Zhang
- 3. Ahmet Kara
- 4. Dan Olteanu
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,157 | ARM-Net: Adaptive Relation Modeling Network for Structured Data | 2021 | SIGMOD | 7.4679349e-05 |
| 4,787 | The Relational Data Borg is Learning | 2020 | VLDB | 5.9168117e-05 |
| 5,973 | Change Propagation Without Joins | 2023 | VLDB | 5.2459364e-05 |
| 8,428 | Insert-Only versus Insert-Delete in Dynamic Query Evaluation | 2024 | PODS | 4.5095504e-05 |
| 9,325 | Powering In-Database Dynamic Model Slicing for Structured Data Analytics | 2024 | VLDB | 4.351469e-05 |
| 9,328 | FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data | 2023 | SIGMOD | 4.351469e-05 |
| 9,855 | In-Database Data Imputation | 2024 | SIGMOD | 4.2652623e-05 |
| 10,794 | Streaming View: An Efficient Data Processing Engine for Modern Real-time Data Warehouse of Alibaba Cloud | 2025 | VLDB | 4.1905499e-05 |
| 11,189 | Regularized Pairwise Relationship based Analytics for Structured Data | 2023 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 585 | DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views | 2012 | VLDB | 0.00019682634 |
| 832 | Learning Linear Regression Models over Factorized Joins | 2016 | SIGMOD | 0.00016089705 |
| 4,200 | Incremental View Maintenance with Triple Lock Factorization Benefits | 2018 | SIGMOD | 6.3618329e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,655 | Query-Driven Learning for Next Generation Predictive Modeling & Analytics | 2019 | SIGMOD | 4.1905499e-05 |
| 6,400 | ColumnML: Column-Store Machine Learning with On-The-Fly Data Transformation | 2019 | VLDB | 5.0739311e-05 |
| 8,847 | Towards Foundation Database Models | 2025 | CIDR | 4.4329366e-05 |
| 1,375 | SQLEM: Fast Clustering in SQL using the EM Algorithm | 2000 | SIGMOD | 0.00012321024 |
| 832 | Learning Linear Regression Models over Factorized Joins | 2016 | SIGMOD | 0.00016089705 |
| 4,787 | The Relational Data Borg is Learning | 2020 | VLDB | 5.9168117e-05 |
| 1,172 | Learning Generalized Linear Models Over Normalized Data | 2015 | SIGMOD | 0.00013504249 |
| 9,775 | Structure-Aware Machine Learning over Multi-Relational Databases | 2021 | SIGMOD | 4.2815042e-05 |
| 4,195 | F: Regression Models over Factorized Views | 2016 | VLDB | 6.3635322e-05 |
| 4,200 | Incremental View Maintenance with Triple Lock Factorization Benefits | 2018 | SIGMOD | 6.3618329e-05 |