Published in IEEE Transactions on Knowledge and Data Engineering, 2025
Operator-level diagnosis of database performance anomalies.
Recommended citation: Huang, S., Wang, Z., Wu, Y., Tu, Y., Wang, J., & Cui, B. (2025). OpDiag: Unveiling Database Performance Anomalies through Query Operator Attribution. IEEE Transactions on Knowledge and Data Engineering. https://ieeexplore.ieee.org/document/10960743
Published in SIGMOD, 2023
A benchmark with the reproduction procedures of the anomalies and a large dataset.
Recommended citation: Huang, S., Wang, Z., Zhang, X., Tu, Y., Li, Z., & Cui, B. (2023). DBPA: A Benchmark for Transactional Database Performance Anomalies. Proceedings of the ACM on Management of Data, 1(1), 1-26. https://dl.acm.org/doi/abs/10.1145/3588926
Published in Science China Information Sciences, 2023
Survey on database performance prediction, diagnosis, and tuning.
Recommended citation: Huang, S., Qin, Y., Zhang, X., Tu, Y., Li, Z., & Cui, B. (2023). Survey on performance optimization for database systems. Science China Information Sciences, 66(2), 121102. https://www.sciengine.com/SCIS/doi/10.1007/s11432-021-3578-6
Published in The VLDB Journal, 2021
Efficient second-order random walk framework (journal extension).
Recommended citation: Shao, Y., Huang, S., Li, Y., Miao, X., Cui, B., & Chen, L. (2021). Memory-aware framework for fast and scalable second-order random walk over billion-edge natural graphs. The VLDB Journal, 30(5), 769-797. https://link.springer.com/article/10.1007/s00778-021-00669-2
Published in SIGMOD, 2020
Efficient second-order random walk framework.
Recommended citation: Shao, Y., Huang, S., Miao, X., Cui, B., & Chen, L. (2020, June). Memory-aware framework for efficient second-order random walk on large graphs. In Proceedings of the 2020 ACM SIGMOD international conference on management of data (pp. 1797-1812). https://link.springer.com/article/10.1007/s00778-021-00669-2