Our research lies at the intersection of Data Management and Artificial Intelligence.
We are the Data+AI Group at Nanyang Technological University (NTU). Our research focuses on Data management and AI, bridging the gap between data management and artificial intelligence. We explore two main directions: DB4AI (Database for AI) and AI4DB (AI for Database).
Our research spans various topics:
TATA: An Efficient Framework for Task Transfer in Query Plan Representation. Yue Zhao, Songsong Mo, Gao Cong.
BT-Tree: A Reinforcement Learning Based Index for Big Trajectory Data. Tu Gu, Kaiyu Feng, Jingyi Yang, Gao Cong, Long Cheng, Rui Zhang.
LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency. Zhaodonghui Li, Haitao Yuan, Huiming Wang, Gao Cong, Lidong Bing.
RankPQO: Learning-to-Rank for Parametric Query Optimization. Songsong Mo, Yue Zhao, Zhifeng Bao, Quanqing Xu, Chuanhui Yang, Gao Cong.
Congratulations to Yang Jingyi and Zhao Yue for publishing the tutorial “Machine Learning for Databases: Foundations, Paradigms, and Open problems” at SIGMOD 2024.
Congratulations to Zhao Yue and Li Zhaodonghui for publishing the paper “A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies” at VLDB 2024.
Congratulations to Jingyi for publishing the paper “PLATON: Top-down R-tree Packing with Learned Partition Policy” at SIGMOD 2024.
Congratulations to Songsong, Yile for publishing the paper “Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries” at SIGMOD 2024.
Congratulations to Zizhong for publishing the paper “Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes” at SIGMOD 2024.