LMFAO: An Engine for Batches of Group-By Aggregates
Summary: An in-memory engine LMFAO for large batches of group-by aggregates over joins, enabling fast data-intensive analytics. Targets ML-style workloads—ridge regression with batch gradient descent, CART decision trees, and RK-means clustering—via optimized batch aggregation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,668 | The LDBC Social Network Benchmark: Business Intelligence Workload | 2023 | VLDB | 6.8591612e-05 |
| 4,787 | The Relational Data Borg is Learning | 2020 | VLDB | 5.9224501e-05 |
| 6,380 | SmartLite: A DBMS-based Serving System for DNN Inference in Resource-constrained Environments | 2024 | VLDB | 5.0893219e-05 |
| 8,982 | Optimizing Inference Serving on Serverless Platforms | 2022 | VLDB | 4.4166105e-05 |
| 9,849 | Reptile: Aggregation-level Explanations for Hierarchical Data | 2022 | SIGMOD | 4.2721228e-05 |
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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 |
|---|---|---|---|---|
| 834 | Learning Linear Regression Models over Factorized Joins | 2016 | SIGMOD | 0.00016135159 |
| 3,277 | A Layered Aggregate Engine for Analytics Workloads | 2019 | SIGMOD | 7.2871625e-05 |
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