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jClarity’s John Oliver: ‘Memory consumption quickly seems to become the limiting factor’

Can you sum up your JAX London session in 140 characters?

Using Big Data, want to apply analytics? This talk will cover working with data and applying good science to get the right answers



Why is the theme of your session important to developers right now? What issues does it tackle?

Machine learning and Big Data are very fashionable topics at the moment. But while many of us have spent years learning about how to write software, not a huge amount of emphasis has been placed on how to collect and understand data. So while there are libraries springing up like mad for machine learning, no amount of good algorithms, large sample sets and fast hardware will get around rubbish in rubbish out.



What are you most looking forward to at JAX London?

"Big data from the LHC commissioning: practical lessons from big science" Certainly looks like another very good session from people who are taking a good scientific approach to Big Data.



How did you get into coding and how old were you when you first started?

Earliest I can remember programming was trying to figure out how Gorillas and Nibbles worked in QBasic. At about 14, dived into C++ then hastily retreated realising I may have dived in a bit too deep and went back to Basic/VB. Started doing most of my own projects after starting to learn electronics at about 17 and creating stuff like home made burglar alarms that would send emails when a window was opened.



Which area, or specific projects, within the industry are catching your eye at the moment?

Vert.x is certainly a fairly big focus for us at the moment. It does free you from many of the painful issues while writing multi-threaded code as well as encouraging you to design programs that are naturally scalable.



What data challenges do we face?

Having tried a number of libraries for data analysis, memory consumption quickly seems to become the limiting factor. So likely even more focus on efficiently scaling out the process of machine learning.



What’s the soundtrack to your work?

Pretty much anything I can tune out, been a reasonable fan of Whisperings solo piano radio, or Soma fm Groove Salad.



And finally, would you rather fight one horse-sized duck or 100 duck-sized horses? Explain your reasoning.

100 duck-sized horses no question, only problem would be getting tired from all the kicking.

John has performed research and development in many languages for 15 years, on various platforms from micro controllers, robots, simulations, desktop applications and web services. Currently he is working at jClarity, applying the tools of machine learning to analyse and diagnose performance problems. John holds a PhD in Engineering from Warwick University working on algorithms for coordinating mobile robotic teams. During his study he performed extensive work on both physical and simulated robotic platforms, competing in a number of national and international robotics competitions.  

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