Interacting with nested data

In this talk I will identify some commonly occurring data manipulation tasks, and introduce strategies to deal with them concisely and generically. By the end, you will be equipped with the tools to

• extend Julia’s notion of multiple indexing to container types other than arrays
• manipulate data by adding new tools beyond Julia’s existing map, filter and reduce functionality to provide generic, higher-order functions for inherently nested data, such as splitting, grouping, combining and joining operations
• represent relational data with in-built Julia types, and manipulate relations with the tools above To be specific, we aim to cover the key features of Indexing.jl, SplitApplyCombine.jl and the somewhat-cheekily-named MinimumViableTables.jl.

Speaker's bio

I am an algorithm and software engineer at Fugro Roames, applying machine learning techniques to big data in order to make sense of and to model the physical world. I have been using Julia since v0.3 for both research and commercial production-at-scale, and am the author of several Julia packages including StaticArrays.