We just found the work of another awesome coffee artist. These tantalizing latte portraits are the work of Japanese latte artist Mattsun, currently treating people to delicious works of caffeinated art in Tokyo. Mattsun began creating drinkable works of art back in 2009 while working at an Italian restaurant. In 2011 he held a very popular solo exhibition, entitled Blue Sky Latte Art, in Dōtonbori, Osaka, Japan. To date he has created over 500 pieces of latter art and hopes to one day own a mobile cafe so that he can use his artwork to “bring smiles to people all across Japan.”

Head over to Mattsun’s website to check out lots more of his tasty artwork. You can also follow him on Twitter via @latte_artist_jk.

[via Design You Trust and Inspire Fusion]

Out. Of. Control.



Google+ Ripple diagrams allow you to see who is sharing and resharing your posts. This lets you discover influencers and track how their connections react to your content.

I spend a lot of my daily social media time in Google+. Using the ripples feature is just one of those tools that…


This is the weekend of videos… I’ve already posted Cassandra Summit’s Bests and Top 5 Presentations from MongoNYC.

Basho has published the majority of the presentations from their RICON East 2013 event. I’ve been lucky to be at Ricon West 2012 and it was a fantastic conference. So I…


by Seth Grimes

Research Magazine, a British publication, has published a text-analytics overview, Write here, write now, with comments from a number of industry and analyst sources, myself included. Author Paul Golden did a nice job with the story, yet there’s more to be said – about…

Lemma what? A guide to Text Processing and Machine Learning API terms


This is a representation of clustering, or a painting, whoa!

After we posted the a list of NLP, Sentiment Analysis, and Machine Learning APIs a while ago, we noticed that some API descriptions require a little bit of digging into, to fully appreciate what these APIs can do.  Here’s an example:

Text analysis API including wordnet synsets,relation extraction,named entity recognition and classification,lemmatization,part of speech tagging,tokenization, and semantic role labeling. 

If you’re not familiar with these words, you could totally miss the features that this API is capable of.

To help with that, we have listed below an explanation to some of these words in the NLP/Machine Learning context; as well as APIs (represented as numbered links) whose descriptions mention these terms.  Hopefully a basic understanding of these terms would help you appreciate what these APIs are capable of.

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… or almost nothing about the field.


A little bit of context: I joined my company with very little knowledge of Machine Learning, so I was not exactly prepared to do a proper job as a data scientist.

Well… that’s where a couple of years at a generalist school like Centrale Paris


Seat of Power: the computer workstation for the person with everything


Forbes published this chart based on Wikibon data:

It’s an $18 billion industry heading to $50 billion in five years, according to tech researchers at Wikibon. Make note of the names in the inner circle. They’re the pure plays with the newest science—and are likely to get gobbled up by the growth-hungry incumbents on the outside.

To save your eyes, in the inner circle:

  • LucidWorks
  • Datameer
  • Kognitio
  • Couchbase
  • Basho
  • Datastax
  • Hortonworks
  • Fractal Analytics
  • Mapr
  • Paraccel (nb: Paraccel has already been acquired by Actian)
  • Guavus
  • Alteryx
  • 10gen
  • 1010data
  • Actian
  • Cloudera
  • Palantir
  • MJ Sigma
  • Opera Solutions
  • Splunk
  • Sisense
  • Rainstor
  • Calpoint
  • Think Big Analytics
  • Aerospike
  • Digital Reasoning

Big Data Industry Atlas

The big data market is still shaping. But soon (not very soon though), we’ll see some clear segments with leaders and challengers. And then…, then we will see a lot of acquisitions and mergers.

Original title and link: Big Data Industry Atlas (NoSQL database©myNoSQL)


Mr Wolfram’s enthusiasm for the data and the science is hard to resist.


Thought I’d share a very interesting post in Fast Company on Data Science - “Lessons From A Crash Course In Data Science

Very interesting read when you have 15 minutes. This is how data driven companies really think. Some interesting examples - including one on travel related spend analytics…