DBScholar

Back to papers

Oracle Workload Intelligence

Summary: Oracle Workload Intelligence (WI) models workloads to infer driving processes without overfitting, surfacing code paths for sequence-aware optimizations. It compares models across time to detect shifts, validated on synthetic workloads and customer benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5078
Venue
SIGMOD
Year
2015
Pagerank
5.5550801e-05
Overall Rank
7,740 | 46.90%
DOI
10.1145/2723372.2742791

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{tran_sigmod15,
        title = {{Oracle Workload Intelligence}},
        author = {Tran, Quoc Trung and Morfonios, Konstantinos and Polyzotis, Neoklis},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2742791},
        url = {https://dl.acm.org/doi/10.1145/2723372.2742791},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
4,053 Database-Agnostic Workload Management 2019 CIDR 6.9355341e-05
4,398 Real-time Workload Pattern Analysis for Large-scale Cloud Databases 2023 VLDB 6.7248611e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers