@techreport{TD:101818,
	att_abstract={{Temporal data pose unique data quality challenges due to the presence of autocorrelations, trends, seasonality, and gaps in the data. Data streams are a special case of temporal data where velocity, volume and variety present additional layers of complexity in measuring the veracity of the data.

In this paper, we discuss a general, widely applicable framework for data quality measurement of streams in a dynamic environment that takes into account the evolving nature of streams. We classify data quality anomalies using four types of constraints, identify violations that could be potential data glitches, and use statistical distortion as a metric for measuring data quality in a near real-time fashion. We illustrate our framework using commercially available streams of NYSE stock prices consisting of aggregates of prices and trading volumes collected every minute over a one year period from November 2011 to November 2012.}},
	att_authors={ds8961, td3863, rd1424},
	att_categories={C_BB.1, C_IIS.6},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE Data Engineering Bulletin, Volume number 39, Issue number: 2. {{, Volume 39}}{{, Issue 2}}{{, 2016-06-15}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101818},
	att_url={http://web1-clone.research.att.com:81/techdocs_downloads/TD:101818_DS1_2016-04-29T17:48:58.216Z.pdf},
	author={Divesh Srivastava and Tamraparni Dasu and Rong Duan},
	institution={{IEEE Data Engineering Bulletin}},
	month={June},
	title={{Data Quality for Temporal Streams}},
	year=2016,
}