Search (35 results, page 1 of 2)

  • × theme_ss:"Data Mining"
  • × type_ss:"a"
  1. Heyer, G.; Läuter, M.; Quasthoff, U.; Wolff, C.: Texttechnologische Anwendungen am Beispiel Text Mining (2000) 0.03
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    Source
    Sprachtechnologie für eine dynamische Wirtschaft im Medienzeitalter - Language technologies for dynamic business in the age of the media - L'ingénierie linguistique au service de la dynamisation économique à l'ère du multimédia: Tagungsakten der XXVI. Jahrestagung der Internationalen Vereinigung Sprache und Wirtschaft e.V., 23.-25.11.2000, Fachhochschule Köln. Hrsg.: K.-D. Schmitz
  2. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.02
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    Date
    7. 3.2019 16:32:22
  3. Hölzig, C.: Google spürt Grippewellen auf : Die neue Anwendung ist bisher auf die USA beschränkt (2008) 0.02
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    Content
    "Vor Google gibt es kein Entrinnen. Nun macht sich die größte Internetsuchmaschine daran, auch gefährliche Grippewellen in den USA vorauszusagen - und das schneller als die US-Gesundheitsbehörde. In den Regionen, in denen die Influenza grassiert, häufen sich erfahrungsgemäß auch die Online-Anfragen im Internet speziell zu diesem Thema. "Wir haben einen engen Zusammenhang feststellen können zwischen Personen, die nach themenbezogenen Informationen suchen, und Personen, die tatsächlich an der Grippe erkrankt sind", schreibt Google. Ein Webtool namens "Google Flu Trends" errechnet aus den Anfragen die Ausbreitung von Grippeviren. Auch wenn nicht jeder Nutzer erkrankt sei, spiegele die Zahl der Anfragen doch ziemlich genau die Entwicklung einer Grippewelle wider. Das belege ein Vergleich mit den Daten der US-Seuchenkontrollbehörde CDC, die in den meisten Fällen nahezu identisch seien. Die Internet-Suchmaschine könne anders als die Gesundheitsbehörde täglich auf aktuelle Daten zurückgreifen. Dadurch sei Google in der Lage, die Grippesaison ein bis zwei Wochen früher vorherzusagen. Und Zeit bedeutet Leben, wie Lyn Finelli sagt, Leiter der Abteilung Influenza der USSeuchenkontrollbehörde: "Je früher wir gewarnt werden, desto früher können wir handeln. Dies kann die Anzahl der Erkrankten erheblich minimieren." "Google Flu Trends" ist das erste Projekt, das Datenbanken einer Suchmaschine nutzt, um einen auftretenden Grippevirus zu lokalisieren - zurzeit nur in den USA, aber weltweite Prognosen wären ein folgerichtiger nächster Schritt. Philip M. Polgreen von der Universität von Iowa verspricht sich noch viel mehr: "Theoretisch können wir diese Flut an Informationen dazu nutzen, auch den Verlauf anderer Krankheiten besser zu studieren." Um das Grippe-Ausbreitungsmodell zu erstellen, hat Google mehrere hundert Milliarden Suchanfragen aus den vergangenen Jahren analysiert. Datenschützer haben den Internetgiganten bereits mehrfach als "datenschutzfeindlich" eingestuft. Die Anwender wüssten weder, was mit den gesammelten Daten passiere, noch wie lange gespeicherte Informationen verfügbar seien. Google versichert jedoch, dass "Flu Trends" die Privatsphäre wahre. Das Tool könne niemals dazu genutzt werden, einzelne Nutzer zu identifizieren, da wir bei der Erstellung der Statistiken lediglich anonyme Datenmaterialien nutzen. Die Muster, die wir in den Daten analysieren, ergeben erst in einem größeren Kontext Sinn." An einer echten Virus-Grippe - nicht zu verwechseln mit einer Erkältung - erkranken weltweit mehrere Millionen Menschen, mehr als 500 000 sterben daran."
    Date
    3. 5.1997 8:44:22
  4. Saz, J.T.: Perspectivas en recuperacion y explotacion de informacion electronica : el 'data mining' (1997) 0.02
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  5. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.01
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    Date
    2. 4.2000 18:01:22
  6. Loh, S.; Oliveira, J.P.M. de; Gastal, F.L.: Knowledge discovery in textual documentation : qualitative and quantitative analyses (2001) 0.01
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  7. Chardonnens, A.; Hengchen, S.: Text mining for cultural heritage institutions : a 5-step method for cultural heritage institutions (2017) 0.01
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    Source
    Everything changes, everything stays the same? - Understanding information spaces : Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin/Germany, 13th - 15th March 2017. Eds.: M. Gäde, V. Trkulja u. V. Petras
  8. Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016) 0.01
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  9. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01
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    Date
    22.11.1998 18:57:22
  10. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  11. Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Huang, J.X.; Jemaa, M.B.: Mining correlations between medically dependent features and image retrieval models for query classification (2017) 0.01
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  12. Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007) 0.01
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  13. Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.01
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  14. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.01
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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  15. Fenstermacher, K.D.; Ginsburg, M.: Client-side monitoring for Web mining (2003) 0.01
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  16. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.01
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  17. Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003) 0.01
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  18. Gluck , M.: Multimedia exploratory data analysis for geospatial data mining : the case for augmented seriation (2001) 0.01
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  19. Whittle, M.; Eaglestone, B.; Ford, N.; Gillet, V.J.; Madden, A.: Data mining of search engine logs (2007) 0.01
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  20. Thelwall, M.; Wilkinson, D.; Uppal, S.: Data mining emotion in social network communication : gender differences in MySpace (2009) 0.01
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