Proteus: Autonomous Adaptive Storage for Mixed Workloads
Summary: Proteus is a distributed HTAP DB that autonomously adapts its storage layout for mixed OLTP/OLAP workloads. It generates storage-aware execution plans and dynamically reformats data instead of duplicating copies, delivering HTAP performance with OLTP/OLAP parity and reduced storage. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michael Abebe (University of Waterloo)
- 2. Horatiu Lazu (University of Waterloo)
- 3. Khuzaima Daudjee (University of Waterloo)
BibTeX Citation
@inproceedings{abebe_sigmod22,
title = {{Proteus: Autonomous Adaptive Storage for Mixed Workloads}},
author = {Abebe, Michael and Lazu, Horatiu and Daudjee, Khuzaima},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517834},
url = {https://dl.acm.org/doi/10.1145/3514221.3517834},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,755 | Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD | 2024 | VLDB | 5.5519655e-05 |
| 8,783 | Tiresias: Enabling Predictive Autonomous Storage and Indexing | 2022 | VLDB | 5.3740362e-05 |
| 10,066 | Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes | 2023 | VLDB | 5.1643809e-05 |
| 10,081 | Rethink Query Optimization in HTAP Databases | 2023 | SIGMOD | 5.158939e-05 |
| 10,325 | Perseus: Achieving Strong Consistency and High Data Freshness for Scalable Geo-distributed HTAP | 2026 | SIGMOD | 5.093636e-05 |
| 10,518 | Breaking the Isolation-Freshness Trade-off: Joint Adaptive Storage Optimization for HTAP Systems | 2026 | VLDB | 5.093636e-05 |
| 11,153 | Native Cloud Object Storage in Db2 Warehouse: Implementing a Fast and Cost-Efficient Cloud Storage Architecture | 2024 | SIGMOD | 5.093636e-05 |
| 11,274 | Partition, Don’t Sort! Compression Boosters for Cloud Data Ingestion Pipelines | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 42 of 42 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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