Noel Welsh - Making Big Data Small: Streaming Algorithms in Scala

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Making Big Data Small: Streaming Algorithms in Scala

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In this talk I'll discuss a class of algorithms,
streaming algorithms, that allow real-time processing of data, scale
extraordinarily well, and are simple to implement. Streaming
algorithms were developed to handle data capacities that exceed our
ability to store them (e.g. the Large Hadron Collider) but turn out to
be a great fit for small teams that want to move quickly on data
analysis projects.

In this talk I'll describe the Bloom filter and Count-Min sketch for
counting item occurrence and heavy hitter and quantile algorithms for
estimating frequency information. If there is time and interest I
might discuss lock-free stochastic gradient descent for learning
classifiers and recommendation systems

Noel has over fifteen years experience in software architecture
and development, and over a decade in machine learning and data
mining. Examples of the projects he’s been involved with include one
of the first commercial products to apply machine learning to the
Internet (eventually acquired by Omniture), a BAFTA award winning
website, and a custom CMS used daily by thousands of students. His
latest ventures are Scala consultancy at Underscore
(underscoreconsulting.com (http://underscoreconsulting.com/)) and data analysis and machine learning at
Myna (mynaweb.com (http://mynaweb.com/))

Noel is an active writer, presenter, and open source contributor. Noel
has a PhD in machine learning from the University of Birmingham.

We will, as always, also be heading to the Slaughtered Lamb (http://www.theslaughteredlambpub.com/) pub afterwards.

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