A neural network based intelligent system for tile prefetching in web map services
Web mapping has become a popular way of distributing interactive digital maps over the internet. Instead of dynamically generating map images on the fly, those can be pre-generated and served from a server-side cache for faster retrieval. However, these caches can grow unmanageably in size when the cartography covers mid to large areas for multiple rendering scales. This forces modest organizations to use partial caches containing just a subset of the total tiles, and makes their services less attractive than other mapping services like Google Maps or Microsoft Bing Maps. This work proposes a neural-network-based intelligent system that predicts which areas are likely to be requested in the future from a catalog of geographic features and a short history of past requests. These priority regions can be used by a tile prefetching policy to achieve an optimal population of the cache. Neural networks are trained and validated using supervised learning with real data-sets from a public nation-wide web map service. Trace-driven simulations demonstrate that accurate long-term predictions, up to 90% in terms of cache-hit ratio, can be obtained with the proposed model by prefetching a low fraction, only the 20% of the total tiles, and with a short training period.
neural networks prefetching web mapping geographic features gis tile cache