Urs Hoelzle from Google gave an invited presentation at NIPS. In the presentation, he strongly advocates interacting with data in a particular scalable manner which is something like the following:

- Make a cluster of machines.
- Build a unified filesystem. (Google uses GFS, but NFS or other approaches work reasonably well for smaller clusters.)
- Interact with data via MapReduce.

Creating a cluster of machines is, by this point, relatively straightforward.

Unified filesystems are a little bit tricky—GFS is capable by design of essentially unlimited speed throughput to disk. NFS can bottleneck because all of the data has to move through one machine. Nevertheless, this may not be a limiting factor for smaller clusters.

MapReduce is a programming paradigm. Essentially, it is a combination of a data element transform (map) and an agreggator/selector (reduce). These operations are highly parallelizable and the claim is that they support the forms of data interaction which are necessary.

Apparently, the Nutch project has an open source implementation of mapreduce (but this is clearly the most nonstandard element).

Shifting towards this paradigm has several effects:

- It makes “big data” applications more viable.
- It makes some learning algorithms more viable than others. One way to think about this is in terms of statistical query learning algorithms. The (generalized) notion of statistical query algorithms is algorithms that rely upon only the results of expections of a (relatively small) number of functions. Any such algorithm can be implemented via mapreduce. The “naive bayes” algorithm and most decision tree algorithms can be easily phrased as statistical query algorithms. Support vector machines can (technically) be phrased as statistical query algorithms, but the number of queries scales with the number of datapoints. Gradient descent algorithms can also be phrased as statistical query algorithms. Learning algorithms which work on one example at a time are not generally statistical query algorithms.
Another way to think about this is in terms of the complexity of the computation. Roughly speaking, as the amount of data scales, only *O(n)* or (perhaps) *O(n log(n))* algorithms are tractable. This strongly favors online learning algorithms. Decision trees and naive bayes are (again) relatively reasonable. Support vector machines (or gaussian processes) encounter difficulties related to scaling.

There is a reasonable argument that the “low hanging fruit” of machine learning research is in the big data with enabling tools paradigm. This is because (a) the amount of data available has been growing far faster than the amount of computation and (b) we just haven’t had the tools to scale here, until recently.

I expect Urs is right: we should look in this direction.