Difference between revisions of "Jiake Ge zh"
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'''邮政编码'''：300354</br> | '''邮政编码'''：300354</br> | ||
'''办公地址'''：55楼A417</br> | '''办公地址'''：55楼A417</br> | ||
| + | '''电子邮箱'''：gejiake@tju.edu.cn | ||
| − | + | ||
| + | == 学术论文 == | ||
| + | |||
| + | |||
| + | #<span style="color: red;">[WWW'25]</span> Hui Wang, Xin Wang, '''Jiake Ge*''', et al. '''ShapeShifter: Workload-Aware Adaptive Evolving Index Structures Based on Learned Models.'''[C]//The 2025 ACM Web Conference. '''(Corresponding author)''' (CCF A) | ||
| + | #[WWW'25] Yiheng You, Xin Wang, Jiake Ge*, et al. KOE: A Key Distribution-Oriented Performance Estimation Framework for Diverse Index Structures.[C]//The 2025 ACM Web Conference, short paper. (Corresponding author) (CCF A) | ||
| + | #[SIGMOD'24] Jiake Ge, Huanchen Zhang, et al. SALI: A Scalable Adaptive Learned Index Framework based on Probability Models[C]//Proceedings of the 2024 ACM international conference on management of data. (CCF A) | ||
| + | #[ICDE'23] Jiake Ge, Boyu Shi, et al. Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes[C]//2023 IEEE 39th International Conference on Data Engineering. (CCF A) | ||
| + | #[JCST'21] Jiake Ge, Yanfeng Chai, Yunpeng Chai. WATuning: a workload-aware tuning system with attention-based deep reinforcement learning[J]. Journal of Computer Science and Technology, 2021. (SCI,CCF B) | ||
| + | #[WWW'24] Yunda Guo, Jiake Ge, et al. PASS: Predictive Auto-Scaling System for Large-scale Enterprise Web Applications[C]// 2024 Proceedings of the ACM on Web Conference. (CCF A) | ||
| + | #[LSGDA'24] Yang Liu, Xin Wang, Jiake Ge, et al. Text to Graph Query Using Filter Condition Attributes[C]// VLDB 2024 Workshop: 3rd International Workshop on Large-Scale Graph Data Analytics. Best Paper. | ||
| + | #[BDR'22] Yanfeng Chai, Jiake Ge, et al. Correlation Expert Tuning System for Performance Acceleration[J]. Big Data Research, 2022, 30: 100345. (SCI) | ||
| + | #[WISE'21] Yanfeng Chai, Jiake Ge, et al. Xtuning: Expert database tuning system based on reinforcement learning[C]// International Conference on Web Information Systems Engineering, 2021: 101-110. (CCF C) | ||
Revision as of 02:14, 19 February 2025
天津大学 智能与计算学部 助理研究员
研究领域：智能数据库技术（AI4DB）、知识图谱、云资源管理技术
研究方向：学习索引、数据库自动调优、知识图谱数据管理、云资源弹性伸缩
通讯地址：天津市海河教育园区天津大学北洋园校区智能与计算学部
邮政编码：300354
办公地址：55楼A417
电子邮箱：gejiake@tju.edu.cn
学术论文
- [WWW'25] Hui Wang, Xin Wang, Jiake Ge*, et al. ShapeShifter: Workload-Aware Adaptive Evolving Index Structures Based on Learned Models.[C]//The 2025 ACM Web Conference. (Corresponding author) (CCF A)
- [WWW'25] Yiheng You, Xin Wang, Jiake Ge*, et al. KOE: A Key Distribution-Oriented Performance Estimation Framework for Diverse Index Structures.[C]//The 2025 ACM Web Conference, short paper. (Corresponding author) (CCF A)
- [SIGMOD'24] Jiake Ge, Huanchen Zhang, et al. SALI: A Scalable Adaptive Learned Index Framework based on Probability Models[C]//Proceedings of the 2024 ACM international conference on management of data. (CCF A)
- [ICDE'23] Jiake Ge, Boyu Shi, et al. Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes[C]//2023 IEEE 39th International Conference on Data Engineering. (CCF A)
- [JCST'21] Jiake Ge, Yanfeng Chai, Yunpeng Chai. WATuning: a workload-aware tuning system with attention-based deep reinforcement learning[J]. Journal of Computer Science and Technology, 2021. (SCI,CCF B)
- [WWW'24] Yunda Guo, Jiake Ge, et al. PASS: Predictive Auto-Scaling System for Large-scale Enterprise Web Applications[C]// 2024 Proceedings of the ACM on Web Conference. (CCF A)
- [LSGDA'24] Yang Liu, Xin Wang, Jiake Ge, et al. Text to Graph Query Using Filter Condition Attributes[C]// VLDB 2024 Workshop: 3rd International Workshop on Large-Scale Graph Data Analytics. Best Paper.
- [BDR'22] Yanfeng Chai, Jiake Ge, et al. Correlation Expert Tuning System for Performance Acceleration[J]. Big Data Research, 2022, 30: 100345. (SCI)
- [WISE'21] Yanfeng Chai, Jiake Ge, et al. Xtuning: Expert database tuning system based on reinforcement learning[C]// International Conference on Web Information Systems Engineering, 2021: 101-110. (CCF C)
