Estimating quantiles from the union of historical and streaming data Singh, Sneha Tirthapura, Srikanta Tirthapura, Srikanta
dc.contributor.department Electrical and Computer Engineering 2018-02-19T07:25:37.000 2020-06-30T02:01:35Z 2020-06-30T02:01:35Z Fri Jan 01 00:00:00 UTC 2016 2018-01-12 2016-11-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>This is a manuscript of a proceeding published as Singh, Sneha Aman, Divesh Srivastava, and Srikanta Tirthapura. "Estimating quantiles from the union of historical and streaming data." Proceedings of the VLDB Endowment 10, no. 4 (2016): 433-444. <a href="" target="_blank">10.14778/3025111.3025124</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1046
dc.identifier.contextkey 11357929
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dc.identifier.submissionpath ece_conf/44
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 00:17:54 UTC 2022
dc.source.uri 10.14778/3025111.3025124
dc.subject.disciplines Electrical and Computer Engineering
dc.title Estimating quantiles from the union of historical and streaming data
dc.type article
dc.type.genre conference
dspace.entity.type Publication
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