Identifying correlated heavy-hitters in a two-dimensional data stream Lahiri, Bibudh Mukherjee, Arko Tirthapura, Srikanta Tirthapura, Srikanta
dc.contributor.department Electrical and Computer Engineering 2018-02-19T07:26:29.000 2020-06-30T02:02:20Z 2020-06-30T02:02:20Z Fri Jan 01 00:00:00 UTC 2016 2016-07-01
dc.description.abstract <p>We consider online mining of correlated heavy-hitters (CHH) from a data stream. Given a stream of two-dimensional data, a correlated aggregate query first extracts a substream by applying a predicate along a primary dimension, and then computes an aggregate along a secondary dimension. Prior work on identifying heavy-hitters in streams has almost exclusively focused on identifying heavy-hitters on a single dimensional stream, and these yield little insight into the properties of heavy-hitters along other dimensions. In typical applications however, an analyst is interested not only in identifying heavy-hitters, but also in understanding further properties such as: what other items appear frequently along with a heavy-hitter, or what is the frequency distribution of items that appear along with the heavy-hitters. We consider queries of the following form: “In a stream <em>S</em> of (<em>x</em>, <em>y</em>) tuples, on the substream <em>H</em> of all <em>x</em> values that are heavy-hitters, maintain those <em>y</em> values that occur frequently with the <em>x</em> values in <em>H</em>”. We call this problem as CHH. We formulate an approximate formulation of CHH identification, and present an algorithm for tracking CHHs on a data stream. The algorithm is easy to implement and uses workspace much smaller than the stream itself. We present provable guarantees on the maximum error, as well as detailed experimental results that demonstrate the space-accuracy trade-off.</p>
dc.description.comments <p>This is an Accepted Manuscript of an article published by Taylor & Francis as Lahiri, Bibudh, Arko Provo Mukherjee, and Srikanta Tirthapura. "Identifying correlated heavy-hitters in a two-dimensional data stream." Data Mining and Knowledge Discovery 30, no. 4 (2016): 797-818. Available online:<a href="" target="_blank">10.1007/s10618-015-0438-6</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1151
dc.identifier.contextkey 11358086
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ece_pubs/148
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 20:26:48 UTC 2022
dc.source.uri 10.1007/s10618-015-0438-6
dc.subject.disciplines Electrical and Computer Engineering
dc.subject.keywords Data stream mining
dc.subject.keywords Correlation
dc.subject.keywords Heavy-hitters
dc.title Identifying correlated heavy-hitters in a two-dimensional data stream
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
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