@techreport{TD:101769,
	att_abstract={{Modern enterprises generate huge amounts of streaming data, for
example, micro-blog feeds, financial data, network monitoring and
industrial application monitoring. While Data Stream Management
Systems have proven successful in providing support for real-time
alerting, many applications, such as network monitoring for intrusion
detection and real-time bidding, require complex analytics
over historical and real-time data over the data streams. We
present a new method to process one of the most fundamental analytical
primitives, quantile queries, on the union of historical and
streaming data. Our method combines an index on historical data
with a memory-efficient sketch on streaming data to answer quantile
queries with accuracy-resource tradeoffs that are significantly
better than current solutions that are based solely on disk-resident
indexes or solely on streaming algorithms.quantile queries, on the union of historical and streaming data. Our method combines an index on historical data with a memory-efficient sketch on streaming data to answer quantile queries with accuracy-resource tradeoffs that are significantly better than current solutions that are based solely on disk-resident indexes or solely on streaming algorithms.}},
	att_authors={ds8961},
	att_categories={C_BB.1, C_IIS.1},
	att_copyright={{VLDB Foundation}},
	att_copyright_notice={{The definitive version was published in Very Large Databases, 2016. Proceedings of the VLDB Endowment}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101769},
	att_url={http://web1-clone.research.att.com:81/techdocs_downloads/TD:101769_DS1_2016-11-24T09:06:32.110Z.pdf},
	author={Divesh Srivastava and Sneha Aman Singh and Srikanta Tirthapura},
	institution={{Proceedings of the VLDB Endowment}},
	month={December},
	title={{Estimating Quantiles from the Union of Historical and Streaming Data}},
	year=2016,
}