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Towards Self-Tuning Data Placement in Parallel Database Systems

Summary: Online self-tuning data placement for shared-nothing parallel DBs to counter skew and evolving access patterns. Index-based migration with a globally height-balanced structure and multi-granularity load tracking enables fast, low-disruption reorg; simulations and Fujitsu AP3000 experiments show scalable throughput recovery. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3249
Venue
SIGMOD
Year
2000
Pagerank
7.4001286e-05
Overall Rank
3,454 | 76.31%
DOI
10.1145/342009.335416

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{lee_sigmod00,
        title = {{Towards Self-Tuning Data Placement in Parallel Database Systems}},
        author = {Lee, Mong Li and Kitsuregawa, Masaru and Ooi, Beng Chin and Tan, Kian-Lee and Mondal, Anirban},
        series = {{SIGMOD} '00},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/342009.335416},
        url = {https://dl.acm.org/doi/10.1145/342009.335416},
        year = {2000}
}

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