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
Authors
- 1. Maximilian Schleich (University of Washington)
- 2. Dan Olteanu (University of Zurich)
BibTeX Citation
@article{schleich_vldb20,
title = {{LMFAO: An Engine for Batches of Group-By Aggregates}},
author = {Schleich, Maximilian and Olteanu, Dan},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2945--2948},
doi = {10.14778/3415478.3415515},
url = {https://doi.org/10.14778/3415478.3415515},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,018 | The LDBC Social Network Benchmark: Business Intelligence Workload | 2023 | VLDB | 7.8473755e-05 |
| 4,128 | The Relational Data Borg is Learning | 2020 | VLDB | 6.8850804e-05 |
| 5,706 | SmartLite: A DBMS-based Serving System for DNN Inference in Resource-constrained Environments | 2024 | VLDB | 6.1144255e-05 |
| 9,000 | Optimizing Inference Serving on Serverless Platforms | 2022 | VLDB | 5.3350531e-05 |
| 10,001 | Reptile: Aggregation-level Explanations for Hierarchical Data | 2022 | SIGMOD | 5.1814573e-05 |
| 10,237 | Factorized and Vectorized Execution: Optimizing Analytical and Semantic Queries over Relations | 2026 | SIGMOD | 5.093636e-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 |
|---|---|---|---|---|
| 536 | Learning Linear Regression Models over Factorized Joins | 2016 | SIGMOD | 0.0001693369 |
| 2,769 | A Layered Aggregate Engine for Analytics Workloads | 2019 | SIGMOD | 8.1465406e-05 |
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