By the year 2025, the amount of digital data generated by both humans and machines is expected to reach hundreds of exabytes – that is over a hundred billion gigabytes.​

We design, as well as study, database management systems and techniques that process, store, manage, and update these large amounts of data efficiently and effectively.

What We Do

Study various types of databases, and the techniques and programming languages used to manage data.
Explore techniques and real-world applications that involve data grouping, analysis, and security.

Sub Areas

Our Research Projects

Quantum Computing and Machine Learning for Combinatorial Optimisation

Stephane BRESSAN

This project combines quantum computing and machine learning to enhance combinatorial problem-solving. Leveraging Neural Network Quantum States (NNQS) and Quantum Annealing (QA) platforms, it aims to optimize Quadratic Unconstrained Binary Optimization (QUBO) for real-world tasks.

Enhancing Legal Document Services with Accessible and Private LLM Technology

HE Bingsheng

This project aims to transform legal document services by developing a local, privacy-preserving Large Language Model (LLM) technology. The research tackles privacy concerns associated with external server processing, seeking to enhance efficiency, speed, and reliability while overcoming computational constraints.

A Holistic Approach for Scale-Independent Query Processing

CHAN Chee Yong

This project aims to design a holistic approach for scale-independent query processing on big data, addressing critical issues like cost-based optimization, cardinality constraints, and join optimization. The goal is to ensure efficient information retrieval regardless of data size, contributing to advancements in big-data applications' database systems.

NUS Digital Twin for Research and Services

HUANG Zhiyong, HE Bingsheng, Anthony TUNG

This project aims to create a virtual twin of the NUS campus integrating the built and natural environment with static and dynamic data for modelling, visualization, simulation, analysis and AI. By creating a high-fidelity model, it harmonizes diverse data sources, optimizing performance for applications including smart transport, utility planning, climate studies and sustainable campus design.

  • TRL 4

SQLancer: Automatic Testing of Database Management Systems


SQLancer automatically finds logic bugs in Database Management Systems (DBMSs). We have used SQLancer to find and report over 500 unique, previously unknown bugs in widely-used DBMSs. In addition, SQLancer has been widely adopted in the industry.

  • TRL 9

Our Research Groups