@techreport{TD:100051,
	att_abstract={{Many applications process high volumes of streaming data.  Examples include Internet traffic analysis, sensor networks, Web server and 
error log mining, financial tickers and on-line trading, real-time mining of telephone call records or credit card transactions, tracking the 
GPS coordinates of moving objects, and analyzing the results of scientific experiments.  In general, a data stream is a data set that is 
produced incrementally over time, rather than being available in full before its processing begins.  Of course, completely static 
data are not practical, and even traditional databases may be updated over time.  However, new problems arise when processing 
unbounded streams in nearly real time.  In this lecture, we survey these problems and their solutions.}},
	att_authors={lg1173},
	att_categories={},
	att_copyright={{Morgan & Claypool}},
	att_copyright_notice={{}},
	att_donotupload={true},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100051},
	att_url={},
	author={Lukasz Golab and M. Tamer Ozsu},
	institution={{Morgan & Claypool, Synthesis Lectures on Data Management}},
	month={June},
	title={{Stream Data Management}},
	year=2010,
}