Blog Statistics from Performancing | Performancing Metrics Web Strategy by Jeremiah Owyang | Social Media, Web Marketing By Jeremiah Owyang, from Silicon Valley In many respects, Silicon Valley sits atop the world. Its growth and influence has made it the globe’s top location for innovation, STEM jobs, IT patents, venture capital funding, and Internet and software growth, and Unicorn startups galore. And yet there’s also been a shift in the Valley’s culture. Growing social and economic rifts have bred fraud, anger and protests. Where housing isn’t in high demand, neighborhoods lay abandoned. One could argue that there’s an emergence of signs that strikingly resemble Detroit in the glory days of the age of transportation. In Detroit’s case, where I visited earlier this week, the Motor City reveled in its dominance in the 1950s, but growing social unrest soon culminated in a massive riot in the late 1960s. Here are four threats, aside from natural disaster, or whole scale physical attack for Silicon Valley today, along with a futuristic probing of their possible conclusions in the coming decades:
Web Mining Unter Web Mining (web mining) auch Webmining versteht man die Übertragung von Techniken des Data-Mining zur (teil)automatischen Extraktion von Informationen aus dem Internet, speziell dem World Wide Web. Web Mining übernimmt Verfahren und Methoden aus den Bereichen Information Retrieval, maschinelles Lernen, Statistik, Mustererkennung und Data-Mining. Dabei können drei Untersuchungsgegenstände unterschieden werden: Arten des Web Minings[Bearbeiten] Web-Usage-Mining versucht Regularitäten in der Benutzung von Webseiten beziehungsweise Webressourcen zu erkennen. Web-Structure-Mining versucht, die einer Webseite beziehungsweise Domäne zugrunde liegende Verweisstruktur zu erkennen. Web-Content-Mining befasst sich mit der Erkennung von Regularitäten in den Inhalten einer Webressource. Siehe auch[Bearbeiten] Literatur[Bearbeiten] Raymond Kosala, Hendrik Blockeel: Web Mining Research: A Survey. Weblinks[Bearbeiten]
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