Our research has been continuously funded by Research Grants Council (RGC) of Hong Kong, as well as supported by the Hong Kong Scholars Program, K. C. Wong Education Foundation, and Tin Ka Ping Foundation. In the past several years, our research group has received over $5 million of funding for more than 20 research and teaching development projects, including 8 General Research Fund (GRF) projects since 2005.

Owing to their superiority in access latency and energy consumption, flash memory drives have recently become a competitive alternative to magnetic hard disks as secondary storage. Consequently, flash-based database systems have been receiving increasing attention from the research community in the past few years. In this project, we plan to investigate a number of optimization techniques for transaction management in flash-based database systems by exploiting the characteristics of flash memory drives (such as out-of-place updates and page reprogramming).

Graph/Network data model has been a powerful tool to model complicated structures such as social networks, network traffic, biological databases and XML documents, among many others. A typical task on graph data is to retrieve substructures embedded. Formally speaking, given a query graph and a graph database, we want to find the subgraph(s), in the data graphs, that is/are isomorphic to the query graph. The current state-of-the-art technique for subgraph isomorphism almost always comprise two phases -- the filtering and verification phases. The filtering phase is often performed by specialized or feature-based indexes. The pruning power of indexes is evidently crucial to the overall performance. Data graphs are often changing or evolving, which may affect the indexes' pruning powers. In this project, we propose to adjust the indexes adaptively, in response to query workloads.

Materialized views have been important in improving query evaluation performance in database applications. Since materialized views are managed as real data, they are subjected to both queries and updates. While queries on materialized views are straightforward, updates on the views are not. Specifically, a view update must be translated into an update on source databases for maintaining consistency between the view and its source databases. Recently, materialized views have also played a crucial role in many emerging applications, which often pose new challenges or opportunities to view updates. This calls for a new investigation on the view update problem with these new database applications.

In this project, we investigate innovative query processing algorithms for flash memory based storage-centric sensor networks. Of particular interest are distributed data management issues under sensor system workload given the unique read/write/erase characteristics of flash memories.

This research project proposes to take data-centric designs to improve performance for distributed monitoring systems. Two issues namely filter management for approximate monitoring and routing optimization for in-network data aggregation are studied under this project.

With a location-aware wireless device, a mobile user can query his/her surroundings (e.g., finding the nearest gas station or all shopping centers within 5 miles) at any place, anytime. This project aims to support such location-based services while protecting users' location privacy.

Energy efficiency is a critical consideration in the design of a wireless sensor network. This project exploits the trade-off between data quality and communication cost to improve energy efficiency in sensor data collection.