Scalability

Team Lead: Simon Wilson

Scalability refers to our ability to perform data analysis and decision making as the size, complexity or arrival rate of our data become large enough to constrain the analyses that we can practically accomplish.  It is a key challenge across data science, driven by our ever-increasing ability to collect data at higher resolution, larger scale and faster rate. 

While we usually think of scalability in terms of resources like computation time or storage, and those are certainly very important in the work conducted in this challenge, there are other resources that can be important such as energy or access to experts who are used to create ground truth or from whom to elicit prior information.  Within the challenge, there is a variety of theoretical and more application-driven projects that find themselves addressing most of these sorts of resource constraint issues.