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HAWK: A Workload-driven Hierarchical Deadlock Detection Approach in Distributed Database System

Summary: HAWK builds a dynamic hierarchical detection tree from a workload-predicted access graph to partition detection into non-overlapping zones. SCC-cut + greedy graph-cut and periodic sampling adapt to workload changes, reducing time/communication overhead and shortening deadlock duration while improving throughput. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14180
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,942 | 24.93%
DOI
10.14778/3748191.3748224

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Authors

BibTeX Citation

@article{zhang_vldb25,
        title = {{HAWK: A Workload-driven Hierarchical Deadlock Detection Approach in Distributed Database System}},
        author = {Zhang, Rongrong and Ye, Zhiwei and Zhu, Jun-Peng and Cai, Peng and Zhou, Xuan and Cai, Dunbo and Qian, Ling},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {10},
        pages = {3682--3694},
        doi = {10.14778/3748191.3748224},
        url = {https://doi.org/10.14778/3748191.3748224},
        year = {2025}
}

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