Open source won. It’s not that an enemy has been vanquished or that proprietary software is dead, there’s not much regarding adopting open source to argue about anymore. After more than a decade of the low-cost, lean startup culture successfully developing on open source tools, it’s clearly a legitimate, mainstream option for technology tools and innovation.

And open source is not just for hackers and startups. A new class of innovative, widely adopted technologies has emerged from the open source culture of collaboration and sharing — turning the old model of replicating proprietary software as open source projects on its head. Think Git, D3, Storm, Node.js, Rails, Mongo, Mesos or Spark.

We see more enterprise and government folks intermingling with the stalwart open source crowd who have been attending OSCON for years. And, these large organizations are actively adopting many of the open source technologies we track, e.g., web development frameworks, programming languages, content management, data management and analysis tools.

We hear fewer concerns about support or needing geek-level technical competency to get started with open source. In the Small and Medium Business (SMB) market we see mass adoption of open source for content management and ecommerce applications — even for self-identified technology newbies.

MySQL appears as popular as ever and remains open source after three years of Oracle control and Microsoft is pushing open source JavaScript as a key part of its web development environment and more explicit support for other open source languages. Oracle and Microsoft are not likely to radically change their business models, but their recent efforts show that open source can work in many business contexts.

Even more telling:

• With so much of the consumer web undergirded with open source infrastructure, open source permeates most interactions on the web.
• The massive, $100 million, GitHub investment validates the open collaboration model and culture — forking becomes normal.

What does winning look like? Open source is mainstream and a new norm — for startups, small business, the enterprise and government. Innovative open source technologies creating new business sectors and ecosystems (e.g., the distribution options, tools and services companies building around Hadoop). And what’s most exciting is the notion that the collaborative, sharing culture that permeates the open source community spreads to the enterprise and government with the same impact on innovation and productivity.

So, thanks to all of you who made the open source community a sustainable movement, the ones who were there when … and all the new folks embracing the culture. I can’t wait to see the new technologies, business sectors and opportunities you create.

Related:

• Economic impact of open source on small business
• Open source community collaboration strategies for the enterprise
• Microsoft opens up

Webstock wasn’t a rock concert, but a gathering of the geek tribes that lived up to its reputation with accomplished speakers, an interesting mix of topics, and a scenic venue on the harbor.

The conference organizers sat the speakers together and they developed a nice camaraderie — unusual for a tech conference — that had them referencing and building on each others’ presentations. While the presenters came from a variety of backgrounds — engineering, design and the arts — the talks were anchored in how how their topics related to web and mobile business processes.

A few useful and instructive themes emerged over the course of the conference (besides presenters using metaphors from Pixar’s “Wall-E”). Sessions by Doug Bowman, Marco Arment, Karen Halvorson, Steve Souders, Frank Chimero, Christine Perfetti, Josh Clark and Michael Lopp most informed the following.

Deliver value early and build fast with a purpose

Best results come from agile-like processes and cultures that start small and iterate fast. Not surprisingly, this common theme was echoed most often by those with startup backgrounds. By limiting features, releasing fast, and learning, products and services can quickly become aligned to user needs. Resisting feature bloat is important to staying fast, especially after initial success and getting exposed to what may be a large number of users.

The agile approach also lets organization stay tuned into serendipity — uses and features that users exploit that were not part of planned use scenarios (Twitter becoming a communications tool for activism is one example). Corollaries to building fast include failing fast, i.e., quickly acknowledging what doesn’t work, and using the scientific method to test and learn.

Scientific method

Elements of the scientific method — hypothesis setting, testing, learning and adjusting — are just as valid for web performance tuning as for user interfaces and understanding comprehension. Embracing the scientific method can help an organization establish a learning culture. It’s not trivial. To gain the most from testing you need familiarity with using data and quantitative analysis, including visualization (charts), statistics, and machine learning.

Web 2.0 Expo San Francisco 2011, being held March 28-31, will examine key pieces of the digital economy and the ways you can use important ideas for your own success.

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Keep everything human-scaled

Acknowledge that the builders and users of products and services are people whose needs should be understood and accommodated. For user interfaces, human scale means keeping operations and processes simple, functionally consistent, cognitively easy, and intuitive. For engineers and managers that means developing strategies that help align efforts, listening to users and making fast build cycles possible. Communicating on a human scale is best done through narrative processes and storytelling, and establishing a communications strategy. Apple products were often offered as examples of simple-to-use, human-scaled designs that delight users.

Communicating with stories

To effectively communicate with users — to share, to guide and convince, to engage — use storytelling processes like narration and cause-and-effect processes. Stories are a way to cut through and make sense of the increasing volumes and ubiquity of data and noise we experience via the web and mobile devices. Humans are wired to understand and remember stories — they make content and interfaces visceral, memorable and viral (i.e., worth repeating). Different speakers evoked storytelling as a way to develop content strategies, to stay connected with others, as an interface metaphor, e.g., using scrolling to represent the passage of time, and, as a way to put data to use.

My background in data and analysis kept me keenly interested in David McCandless’ “Information is Beautiful” session, with its mix of analytics, visually rendered data and storytelling. David does beautiful, playful and insightful infographics that focus on expressing the relative magnitude/scale of large numbers — especially numbers so large they are abstractions to most people — and on telling the stories in the data. His Debtris visualizations show the size of the US and UK deficits using a visual metaphor from the game Tetris to explains the size of the US and UK debt. David’s work is always designed to tell a story, keeping users compelled to drill further and stay engaged. It’s worth noting that David shared some failures and what he learned from them — the scientific method in action. David, who has a journalism background, calls his mix of charts and narration a “charticle” — a concept worth remembering when teasing a story out of data.

Users expect multi-touch and gestural interfaces

Multi-touch and gestural functions should be treated as the primary user interface, not functionality tacked onto conventional interfaces. If touch and gestures can do the job, there’s no reason to keep a keyboard or mouse. Users see multi-touch and gestures as intuitive and the “right” interface for many tasks, expecting them on many devices, not just mobile phones and tablets. Vendors who fail to fully embrace multi-touch and gestures as the primary interface to devices and services do so at their own peril.

Simple is hard

It’s hard work building the right amount of functionality, providing the optimal amount and frequency of content, creating devices that feel right, and designing user interfaces that are natural and “disappear.” Making a simple experience for users, one that takes the cognitive load of how to use a device so they can focus on why they are using the device, takes effort, sweating the details, thinking, testing, and creativity. To succeed at simple, build fast and use scientific learning processes to refine and improve interfaces. Spend as much time taking out what does not work as building on what does.

Find inspiration everywhere

The presenters commonly described creative flashes inspired by their own needs and from their curiosity about topics outside their primary expertise. Creativity often gets sparked by making non-obvious connections.

Build fast, learn, simplify, tell stories, stay curious — taken together, these themes provide a useful framework for quickly building the next generation of services, applications and devices that become the warp and woof of our increasingly digital and mobile lives.

Related:

• Josh Clark on designing for the iPad
• Geeks at work: Interview with Michael Lopp

Machine learning algorithms can help make sense of data by classifying, clustering and summarizing items in a data set. In general, performance has limited the opportunities to apply machine learning to understanding big or messy data sets. Analysts need to factor in time for speeding up off-the-shelf algorithms or even whether a machine learning pass would complete in a timely manner. While using smaller random samples can help mitigate performance issues, some data sets lend themselves to improved results when applied to more data.

Here we share our experience implementing a set-oriented approach to machine learning that led to huge performance increases (more detail is available in a related post at O’Reilly Answers). Applying a set-oriented approach can help you expand the opportunities to gain the benefits of machine learning on larger, unstructured and complex data sets.

We are working with the US Department of Health and Human Services (HHS) on a project to look for trends in demand for jobs related to Electronic Medical Records (EMR) and Health Information Technology (HIT). The twist, and the reason we decided to build a classifier, is that we wanted to separate jobs for those using EMR systems from those building, implementing, running and selling EMR systems. While many jobs easily fit in one of the two buckets, plenty of job descriptions had duties and company descriptions that made classifying the jobs difficult even for humans with domain expertise.

Identifying the approximately 400,000 jobs with EMR and related references was achieved with high accuracy using a regular expression rule-base. All the job description data is stored on a multi-node Greenplum Massively Parallel Processing (MPP) database cluster, running a Postgres engine. Having an MPP database has been critical for analyzing the large, 1.4-billion-record data set we work with — we can generally run investigative queries against the full data set in minutes.

After some discussion, we decided a Naive Bayes classifier seemed appropriate for the task. While there are some Python open source naive bayes classifiers available, such as NLTK and Orange, I decided to use the algorithm in Toby Segaran’s “Programming Collective Intelligence” so I could tweak the code and play with different feature arrangements. Toby does a great job of tying the code to the principles behind the naive bayes algorithm, and I thought that would help with modding and tuning the classifier for our purposes.

We had a tricky data set with categories that could be only subtly different. We wanted the classifier to be fast enough to iterate through the data many times so we could spend enough time training and tuning the algorithm to optimize classifier accuracy. Starting with a training set of 1,800 categorized jobs (phew…) and a random sample of 1,850 jobs, we set to work trying and reviewing different sets of feature combinations.

That’s when it hit me, job descriptions have lots of words that are often not carefully entered. That creates a large set of words and probabilities to work with, slowing down the algorithm. And, the algorithm was written to explain how Naive Bayes works, not for maximum efficiency. With training and classifying the sample data running for more than six hours, we needed to do something to speed up the process to handle all 400,000 records we wanted to classify.

I contacted Daisy Zhe Wang, an EECS doctoral student at UC Berkeley and a consultant at Bayes Informatics, because of her focus on scaling in-database natural language and machine learning algorithms.

Daisy, together with Bayes Informatics founder Milenko Petrovic, developed a set-oriented approach to implementing the Naive Bayes algorithm that treats the data derived from the training set (features (words) and counts) as a single entity, and converting the Naive Bayes algorithm to Python User Defined Functions (UDFs) that, since Greenplum is a distributed database platform, let us parallelize the classifier process.

The result: The training set was processed and the sample data set classified in six seconds. We were able to classify the entire 400,000-record data set in under six minutes — more than a four-orders-of-magnitude records processed per minute (26,000-fold) improvement. A process that would have run for days, in its initial implementation, now ran in minutes! The performance boost let us try out different feature options and thresholds to optimize the classifier. On the latest run, a random sample showed the classifier working with 92% accuracy.

My simple understanding of their algorithm is that training set results are treated like a model and stored as a single row/column in the database. They’re parsed into a permanent Python data structure once, while each job description is parsed into another temporary data structure. The Python UDFs compare the words in the temporary data structure to the words in the model. The result is one database read for each job description and a single write once the probabilities are compared and the classification assignment made. That’s quite a contrast from reading and writing each word in the training set and the unassigned job.

Why does the set-oriented approach to machine learning matter? Performance and scale issues have long been a problem when trying to fully apply machine learning to large or unruly unstructured data sets. Set-oriented machine learning provides a straightforward way to bypass performance roadblocks, making machine learning a viable option for categorizing, clustering or summarizing large data sets or data sets with big chunks of data (e.g., descriptions or items with large numbers of features).

With any data set, speeding up machine learning processes allows quicker iterations through the data. That creates room to run experiments that improve accuracy and more time to focus on and interpret results to gain insights about the data. Quicker processing reduces the risk of applying machine learning to new topics by reducing the time investment to determine if results are worthwhile.

In summary, the performance boost provided by set-oriented machine learning makes for:

• Handling larger and more diverse data sets
• Applying machine learning to a larger set of problems
• Faster turnarounds
• Less risk
• Better focus on a problem
• Improved accuracy, greater understanding and more usable results

Strata: Making Data Work, being held Feb. 1-3, 2011 in Santa Clara, Calif., will cover many similar topics related to big data, machine learning, analytics and visualization.