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
- 2. Horatiu Lazu
- 3. Khuzaima Daudjee
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,990 | Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD | 2024 | VLDB | 4.6117441e-05 |
| 8,774 | Tiresias: Enabling Predictive Autonomous Storage and Indexing | 2022 | VLDB | 4.4559995e-05 |
| 9,917 | Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes | 2023 | VLDB | 4.2561557e-05 |
| 9,937 | Rethink Query Optimization in HTAP Databases | 2023 | SIGMOD | 4.2482599e-05 |
| 10,030 | Perseus: Achieving Strong Consistency and High Data Freshness for Scalable Geo-distributed HTAP | 2026 | SIGMOD | 4.1945683e-05 |
| 10,230 | Breaking the Isolation-Freshness Trade-off: Joint Adaptive Storage Optimization for HTAP Systems | 2026 | VLDB | 4.1945683e-05 |
| 10,934 | Native Cloud Object Storage in Db2 Warehouse: Implementing a Fast and Cost-Efficient Cloud Storage Architecture | 2024 | SIGMOD | 4.1945683e-05 |
| 11,067 | Partition, Don’t Sort! Compression Boosters for Cloud Data Ingestion Pipelines | 2024 | VLDB | 4.1945683e-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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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,424 | Workload Interference Analysis for HTAP* | 2021 | CIDR | 4.1945683e-05 |
| 137 | H-Store: A High-Performance, Distributed Main Memory Transaction Processing System | 2008 | VLDB | 0.00042342967 |
| 4,770 | The Case For Heterogeneous HTAP | 2017 | CIDR | 5.9338845e-05 |
| 1,700 | Bridging the Archipelago between Row-Stores and Column-Stores for Hybrid Workloads | 2016 | SIGMOD | 0.00010858865 |
| 4,326 | Fast Queries Over Heterogeneous Data Through Engine Customization | 2016 | VLDB | 6.288323e-05 |
| 10,030 | Perseus: Achieving Strong Consistency and High Data Freshness for Scalable Geo-distributed HTAP | 2026 | SIGMOD | 4.1945683e-05 |
| 5,005 | Adaptive HTAP through Elastic Resource Scheduling | 2020 | SIGMOD | 5.7641797e-05 |
| 4,284 | HTAP Databases: What is New and What is Next | 2022 | SIGMOD | 6.2914924e-05 |
| 7,688 | Near-Data Processing in Database Systems on Native Computational Storage under HTAP Workloads | 2022 | VLDB | 4.6772837e-05 |
| 9,937 | Rethink Query Optimization in HTAP Databases | 2023 | SIGMOD | 4.2482599e-05 |