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dc.contributor.authorVaithyanathan, Shivakumar
dc.contributor.editorHärder, Theo
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorSchöning, Harald
dc.contributor.editorSchwarz, Holger
dc.date.accessioned2019-01-17T10:36:46Z
dc.date.available2019-01-17T10:36:46Z
dc.date.issued2011
dc.identifier.isbn978-3-88579-274-1
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/19597
dc.description.abstractAs advanced analytics has become more mainstream in enterprises, usability and system-managed performance optimizations are critical for its wide adoption. As a result, there is an active interest in the design of declarative languages in several analytics areas. In this talk I will describe the efforts in IBM around three areas namely Information Extraction, Entity Resolution and Machine Learning. I will detail these efforts, at some length, and also explain the motivation behind some of the design choices made while implementing declarative solutions for the individual areas. I will end with results that demonstrate multiple advantages of the declarative approaches as compared with existing solutions.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-180
dc.titleThe power of declarative languages: from information extraction to machine learningen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages23-23
mci.conference.sessiontitleRegular Research Papers
mci.conference.locationKaiserslautern
mci.conference.date02.-04.03.2011


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