January 23, 2006, 3:40 p.m. PSA 113
Dept. Comp. Sci. & Eng.
Enhancing Traditional Databases to Support Broader Data Management Applications
Traditional relational databases have been extremely successful in managing
transaction-oriented business data. However, the concept of "one size fits
all" is no longer applicable to support various applications with diverse
requirements on data management. In this talk, I will first review the
relational data model and discuss why it can not meet the new requirements in
web and scientific applications. Then I will introduce three new data models
that enhance the relational data model to meet these new application
requirements: XML, data streams and a model for uncertain data. XML, a tree
structured data model, has been established as the standard format for data
exchange over web and has been used to represent certain scientific data. Data
streams appear in many on-line applications, such as sensor, stock and
sports data processing. Uncertain data associated with probabilistic information
arises from scientific experiments, data integration and information
extraction applications. Finally, I will give an overview of my research that
addresses the challenges in these new data models.
Yi Chen is an assistant professor in the Department of Computer Science and
Engineering at Arizona State University. She received her Ph.D. in Computer
and Information Science from the University of Pennsylvania in 2005.
Yi Chen's research interests lie in database systems and data stream management
for web data and scientific data. Her research includes XML data models and
query languages, storage design, indexing, query processing and optimization
techniques, and data integration.
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