- MLDemos — an open-source visualization tool for machine learning algorithms created to help studying and understanding how several algorithms function and how their parameters affect and modify the results in problems of classification, regression, clustering, dimensionality reduction, dynamical systems and reward maximization. (via Mark Alen)
- kiln (GitHub) — open source extensible on-device debugging framework for iOS apps.
- Industrial Internet — the O’Reilly report on the industrial Internet of things is out. Prasad suggests an illustration: for every car with a rain sensor today, there are more than 10 that don’t have one. Instead of an optical sensor that turns on windshield wipers when it sees water, imagine the human in the car as a sensor — probably somewhat more discerning than the optical sensor in knowing what wiper setting is appropriate. A car could broadcast its wiper setting, along with its location, to the cloud. “Now you’ve got what you might call a rain API — two machines talking, mediated by a human being,” says Prasad. It could alert other cars to the presence of rain, perhaps switching on headlights automatically or changing the assumptions that nearby cars make about road traction.
- Unique in the Crowd: The Privacy Bounds of Human Mobility (PDF, Nature) — We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier’s antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual’s privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals. As Edd observed, “You are a unique snowflake, after all.” (via Alasdair Allan)
Machine Learning Demos, iOS Debugging, Industrial Internet, and Deanonymity
- A Quantitative Literary History of 2,958 Nineteenth-Century British Novels: The Semantic Cohort Method (PDF) — This project was simultaneously an experiment in developing quantitative and computational methods for tracing changes in literary language. We wanted to see how far quantifiable features such as word usage could be pushed toward the investigation of literary history. Could we leverage quantitative methods in ways that respect the nuance and complexity we value in the humanities? To this end, we present a second set of results, the techniques and methodological lessons gained in the course of designing and running this project. Even litcrit becoming a data game.
- Easy6502 — get started writing 6502 assembly language. Fun way to get started with low-level coding.
- How Analytics Really Work at a Small Startup (Pete Warden) — The key for us is that we’re using the information we get primarily for decision-making (should we build out feature X?) rather than optimization (how can we improve feature X?). Nice rundown of tools and systems he uses, with plug for KissMetrics.
Barlow's distilled insights regarding the ever evolving definition of real time big data analytics
During a break in between offsite meetings that Edd and I were attending the other day, he asked me, “did you read the Barlow piece?”
“Umm, no.” I replied sheepishly. Insert a sidelong glance from Edd that said much without saying anything aloud. He’s really good at that.
In my utterly meager defense, Mike Loukides is the editor on Mike Barlow’s Real-Time Big Data Analytics: Emerging Architecture. As Loukides is one of the core drivers behind O’Reilly’s book publishing program and someone who I perceive to be an unofficial boss of my own choosing, I am not really inclined to worry about things that I really don’t need to worry about. Then I started getting not-so-subtle inquiries from additional people asking if I would consider reviewing the manuscript for the Strata community site. This resulted in me emailing Loukides for a copy and sitting in a local cafe on a Sunday afternoon to read through the manuscript.
Tips for interacting with analytics colleagues
To quote Pride and Prejudice, businesses have for many years “labored under the misapprehension” that their analytics talent was made up of misanthropes with neither the will nor the ability to communicate or work with others on strategic or creative business problems. These employees were meant to be kept in the basement out of sight, fed bad pizza, and pumped for spreadsheets to be interpreted in the sunny offices aboveground.
This perception is changing in industry as the big data phenomenon has elevated data science to a C-level priority. Suddenly folks once stereotyped by characters like Milton in Office Space are now “sexy.” The truth is there have always been well-rounded, articulate, friendly analytics professionals (they may just like Battlestar more than you), and now that analytics is an essential business function, personalities of all types are being attracted to practice the discipline.
Malware Industrial Complex, Indies Needed, TV Analytics, and HTTP Benchmarking
- Welcome to the Malware-Industrial Complex (MIT) — brilliant phrase, sound analysis.
- Stupid Stupid xBox — The hardcore/soft-tv transition and any lead they feel they have is simply not defensible by licensing other industries’ generic video or music content because those industries will gladly sell and license the same content to all other players. A single custom studio of 150 employees also can not generate enough content to defensibly satisfy 76M+ customers. Only with quality primary software content from thousands of independent developers can you defend the brand and the product. Only by making the user experience simple, quick, and seamless can you defend the brand and the product. Never seen a better put statement of why an ecosystem of indies is essential.
- Data Feedback Loops for TV (Salon) — Netflix’s data indicated that the same subscribers who loved the original BBC production also gobbled down movies starring Kevin Spacey or directed by David Fincher. Therefore, concluded Netflix executives, a remake of the BBC drama with Spacey and Fincher attached was a no-brainer, to the point that the company committed $100 million for two 13-episode seasons.
- wrk — a modern HTTP benchmarking tool capable of generating significant load when run on a single multi-core CPU. It combines a multithreaded design with scalable event notification systems such as epoll and kqueue.
Handmade Hardware, Tab Silencer, Surprise and Models, and Sciencey GIFs
- Your USB Sticks Are Made With Chopsticks (Bunnie Huang) — behind-the-scenes on how USB sticks are made.
- mutetab — find and kill the Chrome tab making all the damn noise! (via Nelson Minar)
- Visualization, Modeling, and Surprises (John D Cook) — paraphrases Hadley Wickham: Visualization can surprise you, but it doesn’t scale well. Modelling scales well, but it can’t surprise you.
- Head Like an Orange — science animated GIFs, assembled from nature documentaries. (via Ed Yong)
A deconstructed web analytics report shows what the dashboard missed.
We can all agree that in 2013 web analytics is still a nightmare, right?
The last few years have brought about an enormous expansion in the top of the web analytics information overload funnel, and today I can discover just about any aspect of my web traffic that piques my curiosity.
I know how much traffic I’m getting, who told them to come here, how they got here, how long they’re staying, what they’re looking at, what they’re using to look at it, where they’re from, and just about anything else I want to know about them. If I don’t like what I’m looking at, I can customize everything from my dashboard to reports to parameters within those reports.
What none of this tells me is how I can be more successful at turning the words I put on the Internet into dollars in my pocket.
Now, I know what you’re thinking: “It’s all there! More information than you could ever figure out what to do with.”
The problem with that is that it’s all there. It’s more information than I could ever figure out what to do with. Read more…
Free Books, Analytics Goofs, Book Boilerplate, and Learn CS with the Raspberry Pi
- Free Book Sifter — lists all the free books on Amazon, has RSS feeds and newsletters. (via BoingBoing)
- Whom the Gods Would Destroy, They First Give Realtime Analytics — a few key reasons why truly real-time analytics can open the door to a new type of (realtime!) bad decision making. [U]ser demographics could be different day over day. Or very likely, you could see a major difference in user behavior immediately upon releasing a change, only to watch it evaporate as users learn to use new functionality. Given all of these concerns, the conservative and reasonable stance is to only consider tests that last a few days or more.
- Web Book Boilerplate (Github) — uses plain old markdown and generates a well structured HTML version of your written words. Since it’s sitting on top of Pandoc and Grunt, you can easily make your books available for every platform. MIT-style license.
- Raspberry Pi Education Manual (PDF) — from Scratch to Python and HCI all via the Raspberry Pi. Intended to be informative and a series of lessons for teachers and students learning coding with the Raspberry Pi as their first device.
Diversity and manageability are big data watchwords for the next 12 months.
Here are some of the key big data themes I expect to dominate 2013, and of course will be covering in Strata.
Emergence of a big data architecture
The coming year will mark the graduation for many big data pilot projects, as they are put into production. With that comes an understanding of the practical architectures that work. These architectures will identify:
- best of breed tools for different purposes, for instance, Storm for streaming data acquisition
- appropriate roles for relational databases, Hadoop, NoSQL stores and in-memory databases
- how to combine existing data warehouses and analytical databases with Hadoop
Of course, these architectures will be in constant evolution as big data tooling matures and experience is gained.
In parallel, I expect to see increasing understanding of where big data responsibility sits within a company’s org chart. Big data is fundamentally a business problem, and some of the biggest challenges in taking advantage of it lie in the changes required to cross organizational silos and reform decision making.
One to watch: it’s hard to move data, so look for a starring architectural role for HDFS for the foreseeable future. Read more…
Win95 Tips, Obama's Big Data, Aggregate Statistics, and Foxconn Robots
- Windows 95 Tips — hilarious tumblr showing the dark side of life through Windows 95 UI tips. (via Juha Saarinen)
- Everything We Know About Obama’s Big Data Operation (Pro Publica) — “White suburban women? They’re not all the same. The Latino community is very diverse with very different interests,” Dan Wagner, the campaign’s chief analytics officer, told The Los Angeles Times. “What the data permits you to do is figure out that diversity.”
- cube (GitHub) — time-series data collection and analysis. Cube lets you compute aggregate statistics post hoc. It also enables richer analysis, such as quantiles and histograms of arbitrary event sets. Cube is built on MongoDB and available under the Apache License on GitHub.
- 1M Robots to Replace 1M Human Jobs at Foxconn (Singularity Hub) — Foxconn plant opening, making manufacturing robots, and they appear to be dogfooding by using them in other plants. $25k each, 10k+ made, and fits into the pattern: the number of operational robots in China increased by 42 percent from 2010 to 2011.