FEATURED STORY

Four short links: 30 June 2015

Four short links: 30 June 2015

Ductile Systems, Accessibility Testing, Load Testing, and CRAP Data

  1. Brittle SystemsMore than two decades ago at Sun, I was convinced that making systems ductile (the opposite of brittle) was the hardest and most important problem in system engineering.
  2. tota11y — accessibility testing toolkit from Khan.
  3. Locustan open source load testing tool.
  4. Impala: a Modern, Open-source SQL Engine for Hadoop (PDF) — CRAP, aka Create, Read, and APpend, as coined by an ex-colleague at VMware, Charles Fan (note the absence of update and delete capabilities). (via A Paper a Day)
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BioBuilder: Rethinking the biological sciences as engineering disciplines

Moving biology out of the lab will enable new startups, new business models, and entirely new economies.

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Buy “BioBuilder: Synthetic Biology in the Lab,” by Natalie Kuldell PhD., Rachel Bernstein, Karen Ingram, and Kathryn M. Hart.

What needs to happen for the revolution in biology and the life sciences to succeed? What are the preconditions?

I’ve compared the biorevolution to the computing revolution several times. One of the most important changes was that computers moved out of the lab, out of the machine room, out of that sacred space with raised floors, special air conditioning, and exotic fire extinguishers, into the home. Computers stopped being things that were cared for by an army of priests in white lab coats (and that broke several times a day), and started being things that people used. Somewhere along the line, software developers stopped being people with special training and advanced degrees; children, students, non-professionals — all sorts of people — started writing code. And enjoying it.

Biology is now in a similar place. But to take the next step, we have to look more carefully at what’s needed for biology to come out of the lab. Read more…

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“Internet of Things” is a temporary term

The O'Reilly Radar Podcast: Pilgrim Beart on the scale, challenges, and opportunities of the IoT.

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Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.

In this week’s Radar Podcast, O’Reilly’s Mary Treseler chatted with Pilgrim Beart about co-founding his company, AlertMe, and about why the scale of the Internet of Things creates as many challenges as it does opportunities. He also talked about the “gnarly problems” emerging from consumer wants and behaviors.

Read more…

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Signals from the O’Reilly 2015 Solid Conference

Insight and analysis on the Internet of Things and the new hardware movement.

Practitioners, entrepreneurs, academics, and analysts came together in San Francisco this week to discuss the Internet of Things and the new hardware movement at the O’Reilly 2015 Solid Conference. Below we’ve assembled notable keynotes and interviews from the event.

Lock in, lock out: DRM in the real world

Author and activist Cory Doctorow uses his Solid keynote to passionately explain how computers are already entwined in our lives and our bodies, which means laws that support lock-in are much more than inconveniences. Doctorow also discusses Apollo 1201, a project from the Electronic Frontier Foundation that aims to eradicate digital rights management (DRM).

Read more…

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The future of car making: Small teams using fewer materials

How we make cars is a bigger environmental issue than how we fuel them.

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Around two billion cars have been built over the last 115 years; twice that number will be built over the next 35-40 years. The environmental and health impacts will be enormous. Some think the solution is electric cars or other low- or zero-emission vehicles. The truth is, if you look at the emissions of a car over its total life, you quickly discover that tailpipe emissions are just the tip of the iceberg.

An 85 kWh electric SUV may not have a tailpipe, but it has an enormous impact on our environment and health. A far greater percentage of a car’s total emissions come from the materials and energy required for manufacturing a car (mining, processing, manufacturing, and disposal of the car ), not the car’s operation. As leading environmental economist and vice chair of the National Academy of Sciences Maureen Cropper notes, “Whether we are talking about a conventional gasoline-powered automobile, an electric vehicle, or a hybrid, most of the damages are actually coming from stages other than just the driving of the vehicle.” If business continues as usual, we could triple the total global pollution generated by automobiles, as we go from two billion to six billion vehicles manufactured.

The conclusion from this is straightforward: how we make our cars is actually a bigger environmental issue than how we fuel our cars. We need to dematerialize — dramatically reduce the material and energy required to build cars — and we need to do it now. Read more…

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The future of data at scale

The O'Reilly Radar Podcast: Turing Award winner Michael Stonebraker on the future of data science.

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Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.

In March 2015, database pioneer Michael Stonebraker was awarded the 2014 ACM Turing Award “for fundamental contributions to the concepts and practices underlying modern database systems.” In this week’s Radar Podcast, O’Reilly’s Mike Hendrickson sits down with Stonebraker to talk about winning the award, the future of data science, and the importance — and difficulty — of data curation.

One size does not fit all

Stonebraker notes that since about 2000, everyone has realized they need a database system, across markets and across industries. “Now, it’s everybody who’s got a big data problem,” he says. “The business data processing solution simply doesn’t fit all of these other marketplaces.” Stonebraker talks about the future of data science — and data scientists — and the tools and skill sets that are going to be required:

It’s all going to move to data science as soon as enough data scientists get trained by our universities to do this stuff. It’s fairly clear to me that you’re probably not going to retread a business analyst to be a data scientist because you’ve got to know statistics, you’ve got to know machine learning. You’ve got to know what regression means, what Naïve Bayes means, what k-Nearest Neighbors means. It’s all statistics.

All of that stuff turns out to be defined on arrays. It’s not defined on tables. The tools of future data scientists are going to be array-based tools. Those may live on top of relational database systems. They may live on top of an array database system, or perhaps something else. It’s completely open.

Read more…

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