The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Automatic Database Management System Tuning Through Large-scale Machine Learning
Open accessAbstract
Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration "knobs" that control everything in the system, such as the amount of memory to use for caches and how often data is written to storage. The problem with these knobs is that they are not standardized (i.e., two DBMSs use a different name for the same knob), not independent (i.e., changing one knob can impact others), and not universal (i.e., what works for one application may be sub-optimal for another). Worse, information about the effects of the knobs typically comes only from (expensive) experience.
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Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
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Cited by
What Goes Around Comes Around... And Around...
Stonebraker, M., & Pavlo, A. (2024) · ACM SIGMOD Record · doi:10.1145/3685980.3685984 · Cited by 29
Self-citationGPTuner: An LLM-Based Database Tuning System
Lao, J., Wang, Y., Li, Y., Wang, J., Zhang, Y., Cheng, Z., Chen, W., Tang, M., & Wang, J. (2025) · ACM SIGMOD Record · doi:10.1145/3733620.3733641 · Cited by 17
Vijaya, J., Paul, S., & Sharma, R. (2025) · Advances in Computational Intelligence and Robotics · doi:10.4018/979-8-3373-1210-1.ch003 · Cited by 5
Metadata
- Authors
- Van Aken, D., Pavlo, A., Gordon, G. J., & Zhang, B.
- Venue
- Proceedings of the 2017 ACM International Conference on Management of Data
- Publisher
- ACM
- Published
- 2017-05-09
- Pages
- 1009-1024
- OpenAlex
- W2613206411
- Cited by
- 573
- References
- 34
- FWCI
- 36.82
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Van Aken, D., Pavlo, A., Gordon, G. J., & Zhang, B. (2017). Automatic Database Management System Tuning Through Large-scale Machine Learning. Proceedings of the 2017 ACM International Conference on Management of Data. https://doi.org/10.1145/3035918.3064029
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
