How to Shrink Big Data

Jelani Nelson, a computer scientist at the University of California, Berkeley, expands the theoretical possibilities for low-memory streaming algorithms. He’s discovered the best procedures for answering on-the-fly questions like “How many different users are there?” (known as the distinct elements problem) and “What are the trending search terms right now?” (the frequent items problem). Nelson’s algorithms often use a technique called sketching, which compresses big data sets into smaller components that can be stored using less memory and analyzed quickly.