In a previous blog post, Real-time data science, I textually described an algorithm that can be used, for example, in real-time data streaming applications to estimate the size (cardinality) of a set.
I just returned from the January, 2014 Meetup of the Boulder/Denver Big Data Meetup group. They stumbled into an amazing format. When they had to postpone their original speaker for a few months, they arranged with four execs, engineers and data scientists from Boulder and Denver companies involved in Big Data and Data Science to each give a 20 minute presentation, followed by a 20 minute roundtable.
Corrgrams, invented and coined by Michael Friendly in his 2002 American Statistician paper are a powerful and rapid way to visualize a dozen or more dimensions simultaneously when in the exploratory phase of multi-variate analysis. (Note that Corrgrams are sometimes erroneously referred to as Correlograms, which are something completely different for time series analysis.)
Looking back on 2013, the world of Hadoop emerged from the era of batch processing and into streaming processing. In the context of "crisp and actionable," actionable often comes with an expiration date. If you don't take action soon enough, it's too late.
I've been harping on the importance of GPUs since my October, 2012 blog post Supercomputing for $500 and more recently in my reviews here of the SC13 conference. A couple of news stories this month indicate broader recognition of the growing importance of "Big Compute".
For my final post on the SC'13 conference that ended this past Friday in Denver, there were two intriguing technologies discussed toward the end.
1. Micon Automata
When I linked my blog post from two days ago, Game changer for HPC: IBM and NVidia novel architecture, on Reddit, it was not well-received by some in the HPC community.