Database Research Projects

 

Enhancing Legal Document Services with Accessible and Private LLM Technology

HE Bingsheng

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

Bryan HOOI

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

Manuel RIGGER

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

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.

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

Manuel RIGGER

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