NSF Org:	IIS Division of Information & Intelligent Systems
Recipient:	THE LELAND STANFORD JUNIOR UNIVERSITY
Initial Amendment Date:	September 19, 2001
Latest Amendment Date:	June 26, 2003
Award Number:	0118173
Award Instrument:	Continuing Grant
Program Manager:	Maria Zemankova IIS  Division of Information & Intelligent Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	October 1, 2001
End Date:	March 31, 2005 (Estimated)
Total Intended Award Amount:	$445,000.00
Total Awarded Amount to Date:	$445,000.00
Funds Obligated to Date:	FY 2001 = $145,000.00 FY 2002 = $150,000.00 FY 2003 = $150,000.00
History of Investigator:	Jennifer Widom (Principal Investigator) widom@cs.stanford.edu Rajeev Motwani (Co-Principal Investigator)
Recipient Sponsored Research Office:	Stanford University 450 JANE STANFORD WAY STANFORD CA  US  94305-2004 (650)723-2300
Sponsor Congressional District:	16
Primary Place of Performance:	Stanford University 450 JANE STANFORD WAY STANFORD CA  US  94305-2004
Primary Place of Performance Congressional District:	16
Unique Entity Identifier (UEI):	HJD6G4D6TJY5
Parent UEI:
NSF Program(s):	INFORMATION & KNOWLEDGE MANAGE
Primary Program Source:	app-0101  app-0102  app-0103
Program Reference Code(s):	9218, HPCC
Program Element Code(s):	685500
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

For many recent applications, the concept of a data stream, possibly infinite, is more appropriate than a data set. By nature, a stored data set is appropriate when significant portions of the data are queried again and again, and updates are small and/or relatively infrequent. In contrast, a data stream is appropriate when the data is changing constantly (often exclusively through insertions of new elements), and it is either unnecessary or impractical to operate on large portions of the data multiple times. The goal of this research project is to develop models and techniques for the management and processing of data streams. Sampling, summarization, and online approximation algorithms will be employed to facilitate query processing and data mining over streams. The results of this project will provide efficient data stream techniques for data management, memory management, query processing, data mining, and data analysis. In addition, a software prototype will be developed for experimentation with algorithms and query processing, and as a testbed for some sample applications of significant scope, such as networking monitoring and traffic engineering, and medical monitoring data.

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