A New Parameter Estimation Method for a Zipf-like Distribution for Geospatial Data Access

摘要

Many reports have shown that the access pattern for geospatial tiles follows Zipf’s law and that its parameter α represents the access characteristics. However, visits to geospatial tiles have temporal and spatial popularities, and the α-value changes as they change. We construct a mathematical model to simulate the user’s access behavior by studying the attributes of frequently visited tile objects to determine parameter estimation algorithms. Because the least squares (LS) method in common use cannot obtain an exact α-value and does not provide a suitable fit to data for frequently visited tiles, we present a new approach, which uses a moment method of estimation to obtain the value of α when α is close to 1. When α is further away from 1, the method uses the associated cache hit ratio for tile access and uses an LS method based on a critical cache size to estimate the value of α. The decrease in the estimation error is presented and discussed in the section on experiment results. This new method, which provides a more accurate estimate of α than earlier methods, promises more effective prediction of requests for frequently accessed tiles for better caching and load balancing.