My name is Igor Carron

## Page Views on Nuit Blanche since July 2010

My papers on ArXiv:
Approximating Kernels at the speed of Light
&
Imaging with Nature

|| Reddit

||
Attendant references pages:
The Advanced Matrix Factorization Jungle Page ||

Paris Machine Learning
@Meetup.com || @Archives

## Wednesday, December 28, 2016

### DataSketches : Sketches Library from Yahoo! - implementation -

While talking to Ravi, I realized I had not mentioned the following library before. Edo was behind the release of the Sketches Library from Yahoo! as open source. This is a Java software library of stochastic streaming algorithms

The Business Challenge: Analyzing Big Data Quickly.
In the analysis of big data there are often problem queries that don’t scale because they require huge compute resources and time to generate exact results. Examples include count distinct, quantiles, most frequent items, joins, matrix computations, and graph analysis.
If approximate results are acceptable, there is a class of specialized algorithms, called streaming algorithms, or sketches that can produce results orders-of magnitude faster and with mathematically proven error bounds. For interactive queries there may not be other viable alternatives, and in the case of real-time analysis, sketches are the only known solution.
For any system that needs to extract useful information from big data these sketches are a required toolkit that should be tightly integrated into their analysis capabilities. This technology has helped Yahoo successfully reduce data processing times from days to hours or minutes on a number of its internal platforms.
This site is dedicated to providing key sketch algorithms of production quality. Contributions are welcome from those in the big data community interested in further development of this science and art.

In particular in the analytics section:

Built-in Theta Sketch set operators (Union, Intersection, Difference) produce sketches as a result (and not just a number) enabling full set expressions of cardinality, such as ((A ∪ B) ∩ (C ∪ D)) \ (E ∪ F). This capability along with predictable and superior accuracy (compared with Include/Exclude approaches) enable unprecedented analysis capabilities for fast queries.

This last paragraph echoes the presentation by Sam Bessalah, on Stream Mining via Abstract Algebra (ppt version). at the last meetup of season 1 of the Paris Machine Learning meetup (Europe Wide Machine Learning Meetup) with Andrew Ng  Sam's abstract was ( I recall distinctly having about 200 people listening studiously to monoids after 2 hours and and half of the other presentation)

A quick introduction into some common algorithms and data structures to handle data in streaming fashion, like bloom filters, hyperloglog or min hashes. Then in a second part how abstract algebra with monoids, groups or semi groups help us reason and build scalable analytical systems beyond stream processing.

Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.