Demo of LearnedWMP: Workload Memory Prediction Using Deep Query Template Representations
Summary: Demo of LearnedWMP for simultaneous workload-wide working-memory prediction using deep query template representations. Shows improved accuracy and faster training/inference over standard per-query estimators, enabling better memory budgeting for in-memory query execution. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Shaikh Quader (York University)
- 2. Ghadeer Abuoda (York University)
- 3. Yonis Abokar (York University)
- 4. Marin Litoiu (York University)
- 5. Manos Papagelis (York University)
BibTeX Citation
@inproceedings{quader_sigmod25,
title = {{Demo of LearnedWMP: Workload Memory Prediction Using Deep Query Template Representations}},
author = {Quader, Shaikh and Abuoda, Ghadeer and Abokar, Yonis and Litoiu, Marin and Papagelis, Manos},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725096},
url = {https://dl.acm.org/doi/10.1145/3722212.3725096},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 18 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059284255 |
| 476 | The Making of TPC-DS | 2006 | VLDB | 0.00017860667 |
| 914 | A Modeling Study of the TPC-C Benchmark | 1993 | SIGMOD | 0.00013246456 |
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