Top-k frequent items and item frequency tracking over sliding windows of any size

Many big data applications today require querying highly dynamic and large-scale data streams to find the top-k most frequent items in the most recent window of a specified size at a specific time. This is a challenging problem. We propose a novel approach called Floating Top-k. Our algorithm does not need to explicitly maintain any item counts over time or deal with count updates upon item entry and expiration. Succinctly , we use only a...