Hazy: Making It Easier to Build and Maintain Big-Data Analytics.
The article discusses the construction and maintenance of Big-Data analytics systems as of March 2013, focusing on machine-learning methods and statistical methods while describing the Hazy project for algorithm management. Topics include trained systems, interfaces between algorithms and systems, t...
| Publicado en: | Communications of the ACM Vol. 56; no. 3; pp. 40 - 50 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Association for Computing Machinery
Mar2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=89061584&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 89061584 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Mar2013 vid: 56 iid: 3 pid: 68 pub: Association for Computing Machinery artinfo: ui: 89061584 10.1145/2428556.2428570 ppf: 40 ppct: 10 formats: tig: atl: Hazy: Making It Easier to Build and Maintain Big-Data Analytics. aug: au: KUMAR, ARUN NIU, FENG RÉ, CHRISTOPHER affil: PhD Student, University of Wisconsin-Madison University of Wisconsin--Madison Indian Institute of Technology-Madras Software Engineer, Google PhD in Computer Science, University of Wisconsin-Madison Tsinghua University Assistant Professor, Dept. of Computer Sciences, University of Wisconsin-Madison PhD, University of Washington su: Big data Machine learning Algorithms Systems design Computer programming System analysis sug: subj: Big data Machine learning Algorithms Systems design Computer programming System analysis ab: The article discusses the construction and maintenance of Big-Data analytics systems as of March 2013, focusing on machine-learning methods and statistical methods while describing the Hazy project for algorithm management. Topics include trained systems, interfaces between algorithms and systems, the GeoDeepDive application for the statistical analysis of geological research papers, and the Bismarck project for infrastructure abstractions. Data sets, probabilistic logic programming, debugging, convex programming, and scalability are mentioned. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2013 holdings: @attributes: islocal: N |
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