@techreport{TD:101358,
	att_abstract={{Big data is a ubiquitous feature of large modern enterprises.
Many organizations generate huge amounts of on-line streaming
data – examples include network monitoring, Twitter feeds,
financial data, and industrial application monitoring. Making
effective use of these data streams can be challenging. While
Data Stream Management Systems can provide support for realtime
alerting and data reduction, many applications require
complex analytics on a data history to best make use of the
streams.
We have been developing technologies for data stream
warehousing, starting with the DataDepot [13] system. A data
stream warehouse continually ingests data streams, computes
complex derived data products, and stores long (perhaps yearslong)
histories. To take advantage of new technologies, we have
developed a next-generation data stream warehousing system. In
this paper we describe the Tidalrace system, our motivations for
developing it, and architectural features of Tidalrace that support
data stream warehousing.}},
	att_authors={tj1857, vs9593},
	att_categories={C_BB.1, C_NSS.2},
	att_copyright={{ACM}},
	att_copyright_notice={{(c) ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2015 {{, 2015-01-06}}.
}},
	att_donotupload={},
	att_private={false},
	att_projects={DataDepot, Darkstar},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101358},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101358_DS1_2014-01-02T20:10:58.647Z.pdf},
	author={Theodore Johnson and Vladislav Shkapenyuk and Marios Hadjieleftheriou},
	institution={{CIDR}},
	month={January},
	title={{Data Stream Warehousing in Tidalrace}},
	year=2015,
}