"Big Data" entries

Four short links: 5 March 2015

Four short links: 5 March 2015

Web Grain, Cognition and Computation, New Smart Watch, and Assessing Accuracy

  1. The Web’s Grain (Frank Chimero) — What would happen if we stopped treating the web like a blank canvas to paint on, and instead like a material to build with?
  2. Bruce Sterling on Convergence of Humans and MachinesI like to use the terms “cognition” and “computation”. Cognition is something that happens in brains, physical, biological brains. Computation is a thing that happens with software strings on electronic tracks that are inscribed out of silicon and put on fibre board. They are not the same thing, and saying that makes the same mistake as in earlier times, when people said that human thought was like a steam engine.
  3. Smart Pocket Watch — I love to see people trying different design experiences. This is beautiful. And built on Firefox OS!
  4. Knowledge-Based Trust (PDF) — Google research paper on how to assess factual accuracy of web page content. It was bad enough when Google incentivised people to make content-free pages. Next there’ll be a reward for scamming bogus facts into Google’s facts database.

Year Zero: Our life timelines begin

In the next decade, Year Zero will be how big data reaches everyone and will fundamentally change how we live.


Editor’s note: this post originally appeared on the author’s blog, Solve for Interesting. This lightly edited version is reprinted here with permission.

In 10 years, every human connected to the Internet will have a timeline. It will contain everything we’ve done since we started recording, and it will be the primary tool with which we administer our lives. This will fundamentally change how we live, love, work, and play. And we’ll look back at the time before our feed started — before Year Zero — as a huge, unknowable black hole.

This timeline — beginning for newborns at Year Zero — will be so intrinsic to life that it will quickly be taken for granted. Those without a timeline will be at a huge disadvantage. Those with a good one will have the tricks of a modern mentalist: perfect recall, suggestions for how to curry favor, ease maintaining friendships and influencing strangers, unthinkably higher Dunbar numbers — now, every interaction has a history.

This isn’t just about lifelogging health data, like your Fitbit or Jawbone. It isn’t about financial data, like Mint. It isn’t just your social graph or photo feed. It isn’t about commuting data like Waze or Maps. It’s about all of these, together, along with the tools and user interfaces and agents to make sense of it.

Every decade or so, something from military or enterprise technology finds its way, bent and twisted, into the mass market. The client-server computer gave us the PC; wide-area networks gave us the consumer web; pagers and cell phones gave us mobile devices. In the next decade, Year Zero will be how big data reaches everyone. Read more…

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Four short links: 3 March 2015

Four short links: 3 March 2015

Wearable Warning, Time Series Data, App Cards, and Secure Comms

  1. You Guys Realize the Apple Watch is Going to Flop, Right? — leaving aside the “guys” assumption of its readers, you can take this either as a list of the challenges Apple will inevitably overcome or bypass when they release their watch, or (as intended) a list of the many reasons that it’s too damn soon for watches to be useful. The Apple Watch is Jonathan Ive’s new Newton. It’s a potentially promising form that’s being built about 10 years before Apple has the technology or infrastructure to pull it off in a meaningful way. As a result, the novel interactions that could have made the Apple watch a must-have device aren’t in the company’s launch product, nor are they on the immediate horizon. And all Apple can sell the public on is a few tweets and emails on their wrists—an attempt at a fashion statement that needs to be charged once or more a day.
  2. InfluxDB, Now With Tags and More UnicornsThe combination of these new features [tagging, and the use of tags in queries] makes InfluxDB not just a time series database, but also a database for time series discovery. It’s our solution for making the problem of dealing with hundreds of thousands or millions of time series tractable.
  3. The End of Apps as We Know ThemIt may be very likely that the primary interface for interacting with apps will not be the app itself. The app is primarily a publishing tool. The number one way people use your app is through this notification layer, or aggregated card stream. Not by opening the app itself. To which one grumpy O’Reilly editor replied, “cards are the new walled garden.”
  4. Signal 2.0Signal uses your existing phone number and address book. There are no separate logins, usernames, passwords, or PINs to manage or lose. We cannot hear your conversations or see your messages, and no one else can either. Everything in Signal is always end-to-end encrypted, and painstakingly engineered in order to keep your communication safe.
Four short links: 2 March 2015

Four short links: 2 March 2015

Onboarding UX, Productivity Vision, Bad ML, and Lifelong Learning

  1. User Onboarding Teardowns — the UX of new users. (via Andy Baio)
  2. Microsoft’s Productivity Vision — always-on thinged-up Internet everywhere, with predictions and magic by the dozen.
  3. Machine Learning Done WrongWhen dealing with small amounts of data, it’s reasonable to try as many algorithms as possible and to pick the best one since the cost of experimentation is low. But as we hit “big data,” it pays off to analyze the data upfront and then design the modeling pipeline (pre-processing, modeling, optimization algorithm, evaluation, productionization) accordingly.
  4. Ten Simple Rules for Lifelong Learning According to Richard Hamming (PLoScompBio) — Exponential growth of the amount of knowledge is a central feature of the modern era. As Hamming points out, since the time of Isaac Newton (1642/3-1726/7), the total amount of knowledge (including but not limited to technical fields) has doubled about every 17 years. At the same time, the half-life of technical knowledge has been estimated to be about 15 years. If the total amount of knowledge available today is x, then in 15 years the total amount of knowledge can be expected to be nearly 2x, while the amount of knowledge that has become obsolete will be about 0.5x. This means that the total amount of knowledge thought to be valid has increased from x to nearly 1.5x. Taken together, this means that if your daughter or son was born when you were 34 years old, the amount of knowledge she or he will be faced with on entering university at age 17 will be more than twice the amount you faced when you started college.

Topic models: Past, present, and future

The O'Reilly Data Show Podcast: David Blei, co-creator of one of the most popular tools in text mining and machine learning.


I don’t remember when I first came across topic models, but I do remember being an early proponent of them in industry. I came to appreciate how useful they were for exploring and navigating large amounts of unstructured text, and was able to use them, with some success, in consulting projects. When an MCMC algorithm came out, I even cooked up a Java program that I came to rely on (up until Mallet came along).

I recently sat down with David Blei, co-author of the seminal paper on topic models, and who remains one of the leading researchers in the field. We talked about the origins of topic models, their applications, improvements to the underlying algorithms, and his new role in training data scientists at Columbia University.

Generating features for other machine learning tasks

Blei frequently interacts with companies that use ideas from his group’s research projects. He noted that people in industry frequently use topic models for “feature generation.” The added bonus is that topic models produce features that are easy to explain and interpret:

“You might analyze a bunch of New York Times articles for example, and there’ll be an article about sports and business, and you get a representation of that article that says this is an article and it’s about sports and business. Of course, the ideas of sports and business were also discovered by the algorithm, but that representation, it turns out, is also useful for prediction. My understanding when I speak to people at different startup companies and other more established companies is that a lot of technology companies are using topic modeling to generate this representation of documents in terms of the discovered topics, and then using that representation in other algorithms for things like classification or other things.”

Read more…

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An alternate perspective on data-driven decision making

The O'Reilly Radar Podcast: Tricia Wang on "thick data," purpose-driven problem solving, and building the ideal team.

In this week’s Radar Podcast episode, O’Reilly’s Roger Magoulas chatted with Tricia Wang, a global tech ethnographer and co-founder of PL Data, about how qualitative and quantitative data need to work together, reframing “data-driven decision making,” and building the ideal team.

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Purpose-driven problem solving

Wang stressed that quantitative and qualitative need to work together. Rather than focusing on data-driven decision making, we need to focus on the best way to identify and solve the problem at hand: the data alone won’t provide the answers:

“It’s been kind of a detriment to our field that there’s this phrase ‘data-driven decision making.’ I think oftentimes people expect that the data’s going to give you answers. Data does not give you answers; it gives you inputs. You still have to figure out how to do the translation work and figure out what the data is trying to explain, right? I think data-driven decision making does not accurately describe what data can do. Really what we should be talking about is purpose-driven problem solving with data. Read more…

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Signals from Strata + Hadoop World in San Jose, CA, 2015

From data-driven government to our age of intelligence, here are key insights from Strata + Hadoop World in San Jose, CA, 2015.

Experts from across the big data world came together for Strata + Hadoop World in San Jose, CA, 2015. We’ve gathered insights from the event below.

U.S. chief data scientist

With a special recorded introduction from President Barack Obama, DJ Patil talks about his new role as the U.S. government’s first ever chief data scientist, the nature of the U.S.’s emerging data-driven government, and defines his mission in leading the data-driven initiative:

“Responsibly unleash the power of data for the benefit of the American public and maximize the nation’s return on its investment in data.”

Read more…

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Exploring methods in active learning

Tips on how to build effective human-machine hybrids, from crowdsourcing expert Adam Marcus.

15146_ORM_Webcast_ad(archived)In a recent O’Reilly webcast, “Crowdsourcing at GoDaddy: How I Learned to Stop Worrying and Love the Crowd,” Adam Marcus explains how to mitigate common challenges of managing crowd workers, how to make the most of human-in-the-loop machine learning, and how to establish effective and mutually rewarding relationships with workers. Marcus is the director of data on the Locu team at GoDaddy, where the “Get Found” service provides businesses with a central platform for managing their online presence and content.

In the webcast, Marcus uses practical examples from his experience at GoDaddy to reveal helpful methods for how to:

  • Offset the inevitability of wrong answers from the crowd
  • Develop and train workers through a peer-review system
  • Build a hierarchy of trusted workers
  • Make crowd work inspiring and enable upward mobility

What to do when humans get it wrong

It turns out there is a simple way to offset human error: redundantly ask people the same questions. Marcus explains that when you ask five different people the same question, there are some creative ways to combine their responses, and use a majority vote. Read more…


Recent performance improvements in Apache Spark

The goal is to offer a single platform where users can get the best distributed algorithms for any data processing task.

2014 has been the most active year of Spark development to date, with major improvements across the entire engine. One particular area where it made great strides was performance: Spark set a new world record in 100TB sorting, beating the previous record held by Hadoop MapReduce by three times, using only one-tenth of the resources; it received a new SQL query engine with a state-of-the-art optimizer; and many of its built-in algorithms became five times faster. In this post, I’ll cover some of the technology behind these improvements as well as new performance work the Apache Spark developer community has done to speed up Spark.

Back in 2010, we at the AMPLab at UC Berkeley designed Spark for interactive queries and iterative algorithms, as these were two major use cases not well served by batch frameworks like MapReduce. As a result, early users were drawn to Spark because of the significant performance improvements in these workloads. However, performance optimization is a never-ending process, and as Spark’s use cases have grown, so have the areas looked at for further improvement. User feedback and detailed measurements helped the Apache Spark developer community to prioritize areas to work in. Starting with the core engine, I’ll cover some of the recent optimizations that have been made. Read more…

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Processing frameworks for Hadoop

How to decide which framework is best for your particular use case.

Editor’s note: Mark Grover will be part of the team teaching the tutorial Architectural Considerations for Hadoop Applications at Strata + Hadoop World in San Jose. Visit the Strata + Hadoop World website for more information on the program.

Hadoop has become the de-facto platform for storing and processing large amounts of data and has found widespread applications. In the Hadoop ecosystem, you can store your data in one of the storage managers (for example, HDFS, HBase, Solr, etc.) and then use a processing framework to process the stored data. Hadoop first shipped with only one processing framework: MapReduce. Today, there are many other open source tools in the Hadoop ecosystem that can be used to process data in Hadoop; a few common tools include the following Apache projects: Hive, Pig, Spark, Cascading, Crunch, Tez, and Drill, along with Impala and Presto. Some of these frameworks are built on top of each other. For example, you can write queries in Hive that can run on MapReduce or Tez. Another example currently under development is the ability to run Hive queries on Spark.

Amidst all of these options, two key questions arise for Hadoop users:

  1. Which processing frameworks are most commonly used?
  2. How do I choose which framework(s) to use for my specific use case?

This post will you help answer both of these questions, giving you enough context to make an educated decision regarding the best processing framework for your specific use case. Read more…

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