Research Interests

  • in-memory database systems
  • database system architectures
  • performance issues
  • distributed database systems
    • epiC

      epiC is an Elastic, Power-aware, data-Intensive Cloud platform. The objectives are to design and implement an efficient multi-tenancy cloud system for supporting high throughput low latency transactions and high performance reliable query processing, with online analytics capability. The whole system contains four main components: VBS, ES2, E3 and BigLog.

    • MemepiC

      MemepiC is the in-memory version of epiC, which not only provides low latency storage service as a distributed key-value store, but also integrates in-memory data analytics functionality to support online analytics. With an efficient data eviction and fetching mechanism, MemepiC has been designed to maintain data that is much larger than the available memory, without severe performance degradation.

    • CIIDAA

      CIIDAA is a large scale, Comprehensive IT Infrastructure for Data-intensive Applications and Analysis. In the project, we aim to harness the power of cloud computing to solve Big Data problems in the real world. The goal is to provide a generic platform to which different cartridges can be plugged in for supporting different applications. To this end, we are investing various areas pertaining big data analysis and the cloud: computing framework, system architecture, performance, security, programming language, software engineering, databases and analytics.

    • LogBase

      The LogBase project aims to develop a scalable log-structured database system. Since log files constitute the only data repository in the system, every aspect of the system, for examples, novel indexing structures and query processing strategies, need to be tailored / redesigned for the new environment.

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