Review for "Materialization Optimizations for Feature Selection Workloads" (SIGMOD'14)

In machine learning, feature selection is the most critical step in data analysis. And based on the survey conducted with industrial analysis, the feature selection is an interactive process. Given this, the authors design a feature-selection language, and also implement it into a R-integaration-based system.

Basically, the user write a standard R program, and directly using the library that provided by the system called "Columbus". After that, the Columbus will use these high-level constructs to optimize the data and computation reuse.

More precisely, Columbus optimize several aspects of feature selections: 1 subsampling, 2 transformation materialization, 3 model caching. This new system also incorporates decomposition (QR decomposition), multiblock logical optimizations and also warmstart methods to optimize the performance.

Trade off: Since user needs to use the high-level columbus libraries, it can be optimized by columbus's optimizer for data and computation reuse. However, it may not enable users to modify or specify some detailed functions within the columbus libraries.

It is a very efficient way for data analysis optimization. I think it will be influential in 10 years.