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Meme Index. Analyzing fads and sensations on the Internet by Miguel Romero at Big Data Spain 2017

Meme Index. Analyzing fads and sensations on the Internet by Miguel Romero at Big Data Spain 2017

The Meme of the Internet Index will be the new normal to analyze and predict facts and sensations which go around the Internet.

https://www.bigdataspain.org/2017/talk/meme-index-analyzing-fads-and-sensations-on-the-internet

Big Data Spain 2017
November 16th - 17th Kinépolis Madrid

Big Data Spain

December 01, 2017
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Transcript

  1. Miguel Romero [short bio] @donkelito • Working in SDG Group.

    • My professional experience. • 7 years as Data Engineer • 6 years as Business Developer and Solution Architect in BIG Data & Advanced Analytics practice • 1 year as Big Data Head • Executive MBA • Not working on my PhD  • Big Data Master Professor • Member of M team. • Agile mode in each instant • Currently: Developing my speaker facet! #BDS15 #BDS17
  2. W TF i s a m em e? • Big

    Data technology has brought an unprecedented explosion in unstructured data. • A meme (neologism modeled on gene) that is a shortening of mimeme (greek) that means imitated thing, is an attempt to explain the way cultural information spreads in terms of evolutionary principles between humans; • Internet memes are – a subset of this general meme concept specific to the culture and environment of the Internet. – a piece of art which spreads from Person to Person via the Internet which carries an additional property that ordinary memes do not (the media through which they propagate that renders them traceable and analyzable)
  3. The Bi g M em e I ndex Analyzing fads

    and sensations on the Internet
  4. tw i tter D ata Sources A PI s #

    m em e Si tes D ata Col l ect D ata Fast A nal yti cs Stream M edi a Processor R D D D Stream FA Stream Media Collector [http source] FA Stream Media Collector [http source] M em e R eposi tory R D D { photo: ’ di stracted- boyfri end’ , sense: ‘ opi ni onchange’ , w ords: [{w : ‘ H I V E’ , p: 1}, {w : ’ D S’ , p: 2}, {w : ’ SPA R K ’ , p: 3}]} # spark # ds # hi ve Enri ch R eposi tory R D D D Stream D ata Servi ces m i croservi ces What do you have? What do you like? Breadth of the Influencers according to trend memes Cultural VS Education VS Trending Stream M edi a B uffer
  5. Template matching is a technique for finding areas of an

    image that match (are similar) to a template image (patch). • Use the OpenCV function matchTemplate to search for matches between an image patch and an input image • Use the OpenCV function minMaxLoc to find the maximum and minimum values (as well as their positions) in a given array Tem pl ate m atchi ng
  6. tesseract.exe staktreak_meme1_youwanadata.png staktreak_meme1_youwanadata.txt --oem 2 -l eng –psm 6 A

    You want data? 3 im} 3~ Here‘s your data O penCV i nterface Tesseract [plain text]
  7. tw i tter D ata Sources A PI s #

    m em e Si tes D ata Col l ect D ata Fast A nal yti cs Stream M edi a Processor R D D D Stream FA Stream Media Collector [http source] FA Stream Media Collector [http source] M em e R eposi tory R D D { photo: ’ di stracted- boyfri end’ , sense: ‘ opi ni onchange’ , w ords: [{w : ‘ H I V E’ , p: 1}, {w : ’ D S’ , p: 2}, {w : ’ SPA R K ’ , p: 3}]} # spark # ds # hi ve Enri ch R eposi tory R D D D Stream D ata Servi ces m i croservi ces What do you have? What do you like? Breadth of the Influencers according to trend memes Cultural VS Education VS Trending Stream M edi a B uffer
  8. Takeaw ay poi nt s • Memes are a vehicle

    whose information could be used as advisory • Memes are unstructured data and until now was not easy to analyze • With Big Data and Advance Analytics technology an architecture to analyze memes (image + text) in NRT or even video on live can be built.
  9. B i g D ata Spai n 2018 i dea!

    W hat i s there i n com m on am ong the m usi c that data-l overs l i sten to?