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James (Zijun) Wang
 
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Current Research Projects.

  • G-SESAME: Gene SEmantic Similarity Analysis and MEasurement tools
    Since many biological data analysis methods require numeric representation of the functional similarity of genes, automatically discovering the descriptive similarities of genes and converting them into measurable numeric values are very important for such analyses. This research project will solve this important problem by designing novel algorithms to measure the semantic similarity of vocabularies used to annotate genes and, in turn, devising effective algorithms to determine the functional similarity of genes.
     
  • P2P Semantic web infrastructure and Ontology comparison.
    In this project, we design an P2P infrastructure for Semantic Web Services to automatically discover the web services based on ontology comparison. An Efficient Online Ontology Comparison Tool has been implemented and used in our P2P Semantic Web Services.
     
  • P2P Cooperative Proxy Caching System
    The goal of this project is to build a self-configured, self-managed P2P cooperative proxy caching system by giving proxies artificial life.
     
  • Virtual Storage System in Corporation Network
    In this project, we will explore the existing commodity storage space in the corporation network and build a virtual file system on top of existing heterogeneous file systems to provide large scale virtual storage system that is robust, self-managed, automatically protected and fast access.
     
  • A P2P multimedia streaming solution
    In this project, we will  provide the multimedia streaming solution at three different levels. In the client level, the buffer management schemes are evaluated to provide a smooth streaming media display. At the proxy cache level, we will use a fragmental caching approach to cache the media streams. In the server level, we use a cluster of commodity PCs as the platform to handle large scale multimedia streaming.
     
  • Multimedia data mining
    Mining the multimedia data is so interesting and we are currently working on a project mining the virtual world using X3D/VRML and MPEG-4.
     

 

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