Database
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
- Anomaly Detection
- Conceptual Modelling
- Data Analysis
- Data Analytics
- Data Clustering
- Databases for Emerging Hardware
- Data Integration
- Data Management
- Data Privacy & Security
- Distributed & Parallel Databases
- End-to-End Data Processing & Analytics
- Graph Mining
- Knowledge Graph
- Query Processing & Optimisation
- Query Reverse Engineering
- Social Media Analysis
- Spatial-Temporal Data Management
- XML
Our Research Projects
Enhancing Legal Document Services with Accessible and Private LLM Technology
This research focuses on developing a local, privacy-preserving Large Language Model (LLM) for legal document services. By eliminating reliance on external servers, the proposed solution enhances user privacy, efficiency, and reliability. The study addresses challenges related to memory and computational constraints through optimisations, aiming to provide accessible and secure document processing.
Enhancing Reliability and Relational Understanding of Visual Language Models
This research aims to address the limitations of Visual Language Models (VLMs), including hallucinations and poor relational understanding. The project proposes benchmarks to evaluate and understand these issues, and develops improved decoding approaches to detect and prevent hallucinations during text generation, enhancing VLM reliability and accuracy.
Validating Relational Database Systems at Scale via Learning-based Automated Testing
This research aims to fully automate the testing of relational database systems, focusing on test-case generation, reduction, and deduplication. By combining traditional and learning-based methods, the project seeks to improve testing efficiency. As part of our ongoing efforts, we have found more than 100 unique, previously unknown bugs in key systems.
Quantum Computing and Machine Learning for Combinatorial Optimisation
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.
A Holistic Approach for Scale-Independent Query Processing
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
