Last Tuesday, I gave a talk at this year’s Berlin Buzzword conference on using stream mining algorithms to efficiently store information extracted from user behavior to perform personalization and recommendation effectively already using a single computer, which is of course key behind streamdrill.
If you’ve been following my talks, you’ll probably recognize a lot of stuff I’ve talked about before, but what is new in this talk is that I tried to take the next step from simply talking about Heavy Hitters and Count- Min Sketches to using these data structures as an approximate storage for all kinds of analytics related data like counts, profiles, or even sparse matrices, as they occur recommendations algorithms.
I think reformulating our approach as basically an efficient approximate data structure also helped to steer the discussion away from comparing streamdrill to other big data frameworks (“Can’t you just do that in Storm?” — “define ‘just’”). As I said in the talk, the question is not whether you can do it in Big Data Framework X, because you probably could. I have started look at it from the other direction: we did not use any Big Data framework and were still able to achieve some serious performance numbers.
In any case, below are the slides and the video from the talk.
Posted by Mikio L. Braun at 2014-05-30 00:00:00 +0200blog comments powered by Disqus