When the folks at ACM SIGMOD asked me to be a guest blogger this month, I figured I should highlight the most community-facing work I’m involved with. So I wrote up a discussion of MADlib, and that the fact that this open-source in-database analytics library is now open to community contributions. (A bunch of us recently wrote a paper on the design and use of MADlib, which made my writing job a bit easier.) I’m optimistic about MADlib closing a gap between algorithm researchers and working data scientists, using familiar SQL as a vector for adoption on both fronts.
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MADlib is an open-source statistical analytics package for SQL that I kicked off last year with friends at EMC-Greenplum. Last Friday we saw it graduate from alpha, to the first beta release version, 0.20beta. Hats off the MADlib team!
Forget your previous associations with low-tech SQL analytics, including so-called “business intelligence”, “olap”, “data cubes” and the like. This is the real deal: statistical and machine learning methods running at scale within the database, massively parallel, close to the data. Much of the code is written in SQL (a language that doesn’t get enough credit as a basis for parallel statistics), with key extensions in C/C++ for performance, and the occasional Python glue code. The suite of methods in the beta includes:
- standard statistical methods like multi-variate linear and logistic regressions,
- supervised learning methods including support-vector machines, naive Bayes, and decision trees
- unsupervised methods including k-means clustering, association rules and Latent Dirichlet Allocation
- descriptive statistics and data profiling, including one-pass Flajolet-Martin and CountMin sketch methods (my personal contributions to the library) to compute distinct counts, range-counts, quantiles, various types of histograms, and frequent-value identification
- statistical support routines including an efficient sparse vector library and array operations, and conjugate gradiant optimization.
More methods are planned for future releases. Myself, I’m working with Daisy Wang on merging her SQL-based Conditional Random Fields and Bayesian inference implementations into the library for an upcoming release, to support sophisticated text processing.
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I often hear that many of the leading data analysts in the field have PhDs in physics or biology or the like, rather than computer science. Computer scientists are typically interested in methods; physical scientists are interested in data.
Another thing I often hear is that a large fraction of the time spent by analysts — some say the majority of time — involves data preparation and cleaning: transforming formats, rearranging nesting structures, removing outliers, and so on. (If you think this is easy, you’ve never had a stack of ad hoc Excel spreadsheets to load into a stat package or database!)
Putting these together, something is very wrong: high-powered people are wasting most of their time doing low-function work. And the challenge of improving this state of affairs has fallen in the cracks between the analysts and computer scientists.
DataWrangler is a new tool we’re developing to address this problem, which I demo’d today at the O’Reilly Strata Conference. DataWrangler is an intelligent visual data transformation tool that lets users reshape, transform and clean data in an intuitive way that surprises most people who’ve worked with data. As you manipulate data in a grid layout, the tool automatically infers information both about the data, and about your intentions for transforming the data. It’s hard to describe, but the lead researcher on the project — Stanford PhD student Sean Kandel — has a quick video up on the DataWrangler homepage that shows how it works. Sean has put DataWrangler live on the site as well.
Tackling these problems fundamentally requires a hybrid technical strategy. Under the covers, DataWrangler is a heady mix of second-order logic, machine learning methods, and human-computer interaction design methodology. We wrote a research paper about it that will appear in this year’s SIGCHI.
If you’re interested in this space, also have a look at Shankar Raman’s very prescient Potter’s Wheel work from a decade ago, the PADS project at AT&T and Princeton, recent research from Sumit Gulwani at Microsoft Research, and David Huynh’s most excellent Google Refine. All good stuff!