Multi-Quelle Heterogenes Grafik Große Daten Repräsentation Lernen
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Multi-Quelle Heterogenes Grafik Große Daten Repräsentation Lernen

Multi-Quelle Heterogenes Grafik Große Daten Repräsentation Lernen


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About the Book

Die große Menge an gesammelten und komplexen Daten stellt auch eine Herausforderung für die Abfrage und Verarbeitung dar. Mit der Aktualisierung der Daten kann die Anzahl der im Graphen enthaltenen Knoten und Kanten immer größer werden. Die Anzahl der Knoten in groß angelegten Graphenstrukturdaten kann Millionen oder sogar Hunderte von Millionen erreichen und weist die Merkmale Multiquelle, Heterogenität, Isomerisierung und Dynamik auf.Heterogene Big Data mit mehreren Quellen lassen sich häufig durch Darstellungslernen in eine Graphdatenstruktur umwandeln. Komplexe Netzwerkgraphen weisen in der Regel bestimmte Besonderheiten auf, die die Forschung erschweren. Das Lernmodell für die Darstellung komplexer heterogener Graphdaten in großem Maßstab hat eine breite Palette von Anwendungen in vielen Bereichen. Dieses Buch befasst sich mit diesen heterogenen Multisource-Graph-Großdatendarstellungs-Lernmodellen sowie mit ihren Anwendungen im Bereich der öffentlichen Sicherheit.


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Product Details
  • ISBN-13: 9786205369708
  • Publisher: KS Omniscriptum Publishing
  • Publisher Imprint: Verlag Unser Wissen
  • Height: 229 mm
  • No of Pages: 188
  • Spine Width: 11 mm
  • Width: 152 mm
  • ISBN-10: 6205369702
  • Publisher Date: 18 Nov 2022
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Weight: 331 gr


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Multi-Quelle Heterogenes Grafik Große Daten Repräsentation Lernen
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