Derrick Harris

Derrick has been a technology journalist since 2003 and has been covering cloud computing, big data and other emerging IT trends for GigaOM since 2009. He has written the words "cloud" and "Hadoop" possibly more than any other person on the planet. Derrick lives in Las Vegas and has a law degree from the University of Nevada, Las Vegas. Away from the office, Derrick trains in muay thai and is active in animal welfare issues.

More stories from Derrick Harris
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http://open.blogs.nytimes.com/2013/11/15/on-the-path-to-personalization/ This post from the New York Times‘ Open blog talks about the architecture and algorithms underpinning its content-personalization engine. Its experience speaks to some larger trends around companies moving from batch to stream processing and to cloud services overall. The Times’ recommendation engine used to […] Read more at GigaOM »

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http://www.nytimes.com/2013/10/07/business/media/nielsen-to-measure-twitter-chatter-about-tv.html Execs are talking about measuring tweet volume and the reach of those tweets, but isn’t the real value in figuring out what people think? It’s not worth touting that 200,000 people tweeted and 4 million people saw those tweets if the overall sentiment is that […] Read more at GigaOM »

http://adage.com/article/media/cox-s-countour-brand-data-recommend-shows/243472/ It’s not so much a new brand as a new offering from Cox Communications, called Contour. People often find Netflix’s recommendations less than ideal, but that’s only $8 a month. I hope it’s the massive DVR and second-screen experience that are supposed to hook users. Read more at GigaOM »

Remember SOPA and PIPA, the two copyright-protection bills that stirred the internet into a frenzy in in late 2011 and early 2012? Well, Harvard’s Berkman Center for Internet & Society just released some really interesting research and an interactive visualization mapping media coverage of the topic […] Read more at GigaOM »

A group of British researchers recently analyzed 2.5 million newspaper articles in order to prove that new data analysis techniques, such as machine learning and natural-language processing, can accurately classify media content. They hope their approach can save academicians untold hours of manual labor. Read more at GigaOM »