Search (57 results, page 1 of 3)

  • × theme_ss:"Data Mining"
  1. Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999) 0.04
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    Date
    17. 7.2002 19:22:06
    Type
    m
  2. Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006) 0.03
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    Date
    22. 3.2008 14:46:06
    Type
    m
  3. 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
  4. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.02
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    Date
    2. 4.2000 18:01:22
  5. 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
  6. KDD : techniques and applications (1998) 0.02
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
  7. Medien-Informationsmanagement : Archivarische, dokumentarische, betriebswirtschaftliche, rechtliche und Berufsbild-Aspekte ; [Frühjahrstagung der Fachgruppe 7 im Jahr 2000 in Weimar und Folgetagung 2001 in Köln] (2003) 0.02
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    Date
    11. 5.2008 19:49:22
    Editor
    Englert, M. u.a.
    Type
    m
  8. 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
  9. 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
  10. Handbuch Web Mining im Marketing : Konzepte, Systeme, Fallstudien (2002) 0.01
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    Type
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  11. Mattison, R.: Data warehousing and data mining for telecommunications (1997) 0.01
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  12. 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
  13. Data Mining im praktischen Einsatz : Verfahren und Anwendungsfälle für Marketing, Vertrieb, Controlling und Kundenunterstützung (2000) 0.01
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  14. Cios, K.J.; Pedrycz, W.; Swiniarksi, R.: Data mining methods for knowledge discovery (1998) 0.01
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  15. Witten, I.H.; Frank, E.: Data Mining : Praktische Werkzeuge und Techniken für das maschinelle Lernen (2000) 0.01
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  16. Intelligent information processing and web mining : Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane, Poland, June 2-5, 2003 (2003) 0.01
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  17. Information visualization in data mining and knowledge discovery (2002) 0.01
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    Date
    23. 3.2008 19:10:22
    Type
    m
  18. Ester, M.; Sander, J.: Knowledge discovery in databases : Techniken und Anwendungen (2000) 0.01
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  19. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01
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    Date
    22. 3.2013 19:43:01
  20. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

Years

Languages

  • e 43
  • d 14

Types