@techreport{TD:100239,
	att_abstract={{A stream warehouse is a Data Stream Management System (DSMS) that stores a very long history, e.g. years or decades; or equivalently a data warehouse that is continuously loaded.  A stream warehouse enables queries that seamlessly range from real-time alerting and diagnostics to long-term data mining.  However, continuously loading data from uncontrolled sources into a real-time stream warehouse introduces a new consistency problem: users want results in as timely a fashion as possible, but �stable� results often require lengthy synchronization delays.  In this paper we develop a theory of consistency for stream warehouses that allows for multiple consistency levels, we show how to restrict query answers to a given consistency level, and we show how warehouse maintenance can be optimized using knowledge of the consistency levels required by materialized views.}},
	att_authors={lg1173, tj1857},
	att_categories={},
	att_copyright={{}},
	att_copyright_notice={{}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100239},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100239_DS1_2010-10-14T17:24:00.478Z.pdf},
	author={Lukasz Golab and Theodore Johnson},
	institution={{CIDR conference}},
	month={January},
	title={{Consistency in a Stream Warehouse}},
	year=2011,
}