Jonathan Stray
Visualization is great way to understand data, but it breaks down when the data gets big. Simply plotting everything to the screen won’t work, because there isn’t enough screen real estate, interactions slow to a crawl, and human working memory isn’t up to the task anyway. Big data requires specific interaction techniques for visual exploration, such as filtering, summarization, and context. We’ll go over some basic principles, and I’ll show examples of recent systems, including our work on the Overview Project, a system for visual exploration of huge unstructured document sets.
About
Jonathan Stray believes in public access to information, hacking for the pleasure of it, and tropical weather. He has an MSc in computer science from the University of Toronto and began his career as an engineer on the Adobe After Effects team in San Francisco. Then he abruptly moved to Hong Kong, picked up an MA in journalism, and worked as a freelance reporter, contributing to the New York Times, Foreign Policy, and Wired. From 2010 to 2012 he was tech lead for interactive news stories at the Associated Press in New York, transitioning the team from Flash to responsive design HTML. He now heads the Overview Project, a Knight News Challenge-funded semantic visualization system for very large document sets, for the benefit of investigative journalists and other curious people.