Towards instance-optimized data systems
Summary: Progress report on ML-for-systems research toward instance-optimized data systems that learn workload and data distributions to self-tune algorithms and structures. Assesses practical deployment readiness through DSAIL/MIT projects, beyond lab-scale performance claims. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tim Kraska (Massachusetts Institute of Technology)
BibTeX Citation
@article{kraska_vldb21,
title = {{Towards instance-optimized data systems}},
author = {Kraska, Tim},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {3222--3232},
doi = {10.14778/3476311.3476392},
url = {https://doi.org/10.14778/3476311.3476392},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,373 | High-Throughput Vector Similarity Search in Knowledge Graphs | 2023 | SIGMOD | 0.00010891169 |
| 3,948 | Deploying a Steered Query Optimizer in Production at Microsoft | 2022 | SIGMOD | 6.9051584e-05 |
| 5,043 | LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems | 2022 | SIGMOD | 6.2979214e-05 |
| 8,402 | The Case for Learned In-Memory Joins | 2023 | VLDB | 5.3375308e-05 |
| 9,601 | Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs | 2024 | SIGMOD | 5.153951e-05 |
| 10,048 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 5.0896901e-05 |
| 10,172 | PLATON: Top-down R-tree Packing with Learned Partition Policy | 2023 | SIGMOD | 5.0658661e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 50 of 51 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 7 | 7,604 | SageDB: An Instance-Optimized Data Analytics System | 2022 | VLDB |
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| 9 | 13,694 | Reimagining Deep Learning Systems Through the Lens of Data Systems | 2024 | VLDB |
| 10 | 6,081 | From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems | 2019 | SIGMOD |