JuliaCon 2017

Many projects in research and development require analysis of tabular data. For example, medical records can be viewed as a collection of variables like height, weight, and age for different patients. The values may be boolean (yes or no), numerical (100.3), categorical (A, B, O), or ordinal (early, middle, late). Some values may also be missing. However, analysis and feature extraction is made easier by knowing relationships between variables, for example, that weight increases with height. GraphGLRM is a framework that leverages structure in data to de-noise, compress, and estimate missing values. Using Julia’s flexibility and speed, we developed this package quickly and with sufficient performance for real-world data processing needs. GraphGLRMs are now robust and versatile enough to work with sparse, heterogeneous data. We will also discuss updates to Julia data structures and tooling that would ease package development and further empower the GraphGLRM framework.    More about GraphGLRMs: https://github.com/mihirparadkar/GraphGLRM.jl    More about LowRankModels: https://github.com/madeleineudell/LowRankModels.jl