The proposed project aims to investigate a new way of visualizing large scientific datasets using high performance networks and a data-distributed visualization paradigm. Large dataset visualization is considered as one of the most challenging problems in visualization today. The emergence of high performance networks offers potentially very powerful solutions and new insight to this problem and related applications. The main objective of this project is to develop new visualization
algorithms and applications that dynamically and efficiently access and process different pieces of the required data over high speed networks. This is done through either remote disk access or direct data stream connections using data servers. Java and Java3D will be used to facilitate run-time data retrieval and platform independence. Since local copies of the datasets are no longer needed, scalability can be more easily achieved by demand-driven data retrieval and efficient dataset decomposition. New visualization algorithms will be developed to accommodate the required data retrieval paradigm. Overall, we aims to achieve total network transparency at both the dataset level and the application level. Applications using this method will be studied and experimented using the Visible Human datasets and time-varying 3D microscopy datasets.
The success of this project will not only lead to new platform independent visualization applications that are less depend on local memory resources, but also significantly reduce the unnecessary data redundancy generated by the large number of local copies of popular datasets. The high level of network transparency and data sharing also lead to an ideal environment for collaborative
applications.
Contact:
Shiaofen Fang
Department of Computer and Information Science, IUPUI
723 W. Michigan St., SL 280
Indianapolis, IN 46202
Email: sfang@cs.iupui.edu
Tel: 274-9731
Fax: 274-9742
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