Why Zipf's law explains so many big data and physics phenomenons

The Zipf's law states that in many settings (that we are going to explore), the volume or size of entities is inversely proportional to a power s (s 0) of their ranking. This has important implications in predictive modeling, discussed below. The processes that create this type of dynamic are not well understood. It is the purpose of this article to explain the underlying mechanics. The traditional example for the Zipf distribution is the distribution of Internet domains, ranked by traffic.