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Analyzing an Isle of Man TT legend

October 9, 2015 By editor

The BBC has an article on using a sensor array to determine what makes 23-time Isle of Man TT winner John McGuinness so quick.

Motorcycle riders have been tackling the 38-mile street circuit for over a hundred years. As it’s run on (closed) public roads, it’s an incredibly dangerous race. Riders average 212kph (132mpg) round the course—often coming within inches of stone walls and buildings.

John McGuinness is one of the most successful riders ever to tackle the course and data analysis company EMC decided to find out why he’s so quick. They fitted 50 sensors to him and the bike—collecting data on speed, acceleration, lean angle, throttle use, braking, body position, heart rate, etc.

One key finding is that he’s as cool as a cucumber, recording a heart rate of a mere 120 beats per minute when travelling at over 300kph on country roads. That lets him conserve energy and concentrate more than less skilled riders.

Applying machine learning techniques to the data revealed that only 14 variables were influential in his performance. McGuinness was just a little bit better in most areas than other riders, but each of these small advantages added up to a significant difference in lap times.

Filed Under: Big data, Data analysis, Data science

Prajna—Microsoft’s response to Spark

September 29, 2015 By editor

Microsoft are developing an open-source distributed analytics platform—codename “Prajna”.

It’s apparently inspired by Spark, but will also make it easy for developers to deploy cloud servies that exploit the processing capabilities of the platform.

It’s written in F# and the project is hosted on GitHub.

Filed Under: Big data Tagged With: F#

Spark 1.5.0 released

September 9, 2015 By editor

Spark 1.5.0 has now been released—and it’s a significant one for the data science community. Databricks, in their announcement blog post, state

Another major theme of this release is data science: Spark 1.5 ships several new machine learning algorithms and utilities, and extends Spark’s new R API.

Improvements of note include better coverage for the pipeline API and an MLlib API for SparkR.

Filed Under: Big data, Data science

A walk through a Spark Random Forest

August 3, 2015 By editor

Learning Tree International have just published one of my articles on using Random Forest models with Spark.

Filed Under: Big data, Data science

R on Spark

June 9, 2015 By editor

The upcoming Apache Spark 1.4 release will include SparkR—an R package that will allow big data (Spark) analyses to be run from the R shell. Computations in SparkR will be comparable to those that use the native Scala language.

Future developments are to include machine learning support.

Filed Under: Big data, Data analysis

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