Active and Accelerated Learning of Cost Models for Optimizing Scientific Applications
Summary: NIMO learns cost models for predicting execution times of scientific workflows on grids. It uses active, noninvasive sampling with passive instrumentation to train from few runs, cutting data needs and learning time. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Piyush Shivam (Duke University)
- 2. Shivnath Babu (Duke University)
- 3. Jeff Chase (Duke University)
BibTeX Citation
@article{shivam_vldb06,
title = {{Active and Accelerated Learning of Cost Models for Optimizing Scientific Applications}},
author = {Shivam, Piyush and Babu, Shivnath and Chase, Jeff},
journal = {PVLDB},
series = {{VLDB} '06},
pages = {535--546},
doi = {10.14778/1164135.1164184},
url = {https://doi.org/10.14778/1164135.1164184},
year = {2006}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,250 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD | 0.00011485301 |
| 2,177 | The Case for Predictive Database Systems: Opportunities and Challenges | 2011 | CIDR | 9.0159844e-05 |
| 4,731 | Automated and On-Demand Provisioning of Virtual Machines for Database Applications | 2007 | SIGMOD | 6.5346682e-05 |
| 12,505 | Large-Scale Uncertainty Management Systems: Learning and Exploiting Your Data (Tutorial Summary) | 2009 | 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 |
|---|---|---|---|---|
| 2,589 | Statistical Learning Techniques for Costing XML Queries | 2005 | VLDB | 8.371643e-05 |
| 6,598 | ZOO: A Desktop Experiment Management Environment | 1997 | SIGMOD | 5.8261133e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 2,589 | Statistical Learning Techniques for Costing XML Queries | 2005 | VLDB |
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| 3 | 6,323 | Modeling Shifting Workloads for Learned Database Systems | 2024 | SIGMOD |
| 4 | 6,088 | How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks | 2025 | SIGMOD |
| 5 | 2,844 | Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction | 2022 | VLDB |
| 6 | 11,065 | Learned Cost Models for Query Optimization: From Batch to Streaming Systems | 2025 | VLDB |
| 7 | 2,822 | Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings | 2020 | SIGMOD |
| 8 | 6,921 | Rethinking Learned Cost Models: Why Start from Scratch? | 2023 | SIGMOD |
| 9 | 6,889 | An Algebraic Approach for Data-Centric Scientific Workflows | 2011 | VLDB |
| 10 | 4,731 | Automated and On-Demand Provisioning of Virtual Machines for Database Applications | 2007 | SIGMOD |