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Database Workload Capacity Planning using Time Series Analysis and Machine Learning

Summary: Time-series analysis with supervised ML to forecast resources for deeper DB layers - clustered DBs, apps, and transaction groups. Identifies recurring patterns and shocks to reduce forecast complexity and improve accuracy for complex workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5962
Venue
SIGMOD
Year
2020
Pagerank
5.8066637e-05
Overall Rank
6,673 | 54.22%
DOI
10.1145/3318464.3386140

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{higginson_sigmod20,
        title = {{Database Workload Capacity Planning using Time Series Analysis and Machine Learning}},
        author = {Higginson, Antony S. and Dediu, Mihaela and Arsene, Octavian and Paton, Norman W. and Embury, Suzanne M.},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3386140},
        url = {https://dl.acm.org/doi/10.1145/3318464.3386140},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
4,327 Dynamic Workload Management for Very Large Data Warehouses - Juggling Feathers and Bowling Balls 2007 VLDB 6.7574155e-05
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