Mathematical Analysis of Large Data Sets

Monday, January 23, 2006, 3:40 p.m. PSA 113

Yi Chen

Dept. Comp. Sci. & Eng.

Enhancing Traditional Databases to Support Broader Data Management Applications

Abstract 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 However, the concept of "one size fits all" is no longer applicable to support various applications with diverse requirements on data management.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.

For further information please contact: Anne Gelb