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Friday October 30, 2009. 4:00 pm
Room 302 HRBB
Database Support for Uncertain Data
Rahul Shah.
Department of Computer Science, Louisiana State University
Abstract
As computer systems scale to handle data emerging from various real-life sources, many new challenges arise. One of the main hurdle is that data is often noisy and inherently uncertain. If it is a sensor reading, it might be stale or prone to measurement errors. So is the case with mobile users data. Many times intelligence about data comes from web-sources and natural language processing engines working on them, inherently generate uncertain data. Many times this data is cleansed before it is inserted into database. This results in not only loss of information but also incorrectness in results. The newer approach attempts to treat uncertain data as "first-class-citizens" in databases.
This leads to several new challenges. I will introduce various aspects of such an uncertain database system. I shall talk about probabilistic modeling under "possible worlds" semantic model to achieve completeness and closed properties under select/project/join operations. The talk will also focus on efficiency aspects in query processing on such uncertain data.
Biography
Rahul Shah is an assistant professor at Louisiana State University. He did his B. Tech from Indian Institute of Technology, Bombay and his PhD from Rutgers University. After his doctoral studies, he was a research assistant professor at Purdue University and research staff member at IBM Research India. He is currently active in various research field like compressed text indexing, uncertain databases and parallel disk models for massive data sets.
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