An OLS regression model for context-aware tile prefetching in a web map cache

The increasing popularity of web map services has motivated the development of more scalable services in the spatial data infrastructures. Tiled map services have emerged as a scalable alternative to traditional map services. Instead of rendering map images on the fly, a collection of pre-generated...

Full description

Saved in:
Bibliographic Details
Published in:International journal of geographical information science : IJGIS 2013-03, Vol.27 (3), p.614-632
Main Authors: García Martín, Ricardo, de Castro Fernández, Juan Pablo, Verdú Pérez, Elena, Verdú Pérez, María Jesús, Regueras Santos, Luisa María
Format: Article
Language:English
Subjects:
geographic features
Geographic information systems
Mapping
OLS
prefetching
Quality of service
Regression analysis
regressive model
tile cache
web mapping
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The increasing popularity of web map services has motivated the development of more scalable services in the spatial data infrastructures. Tiled map services have emerged as a scalable alternative to traditional map services. Instead of rendering map images on the fly, a collection of pre-generated image tiles can be served very fast from a server-side cache. However, during the start-up of the service, the cache is initially empty and users experience a poor quality of service. Tile prefetching attempts to improve hit rates by proactively fetching map images without waiting for client requests. While most popular prefetching policies in traditional web caching consider only the previous access history to make predictions, significant improvements could be achieved in web mapping by taking into account the background geographic information. This work proposes a regressive model to predict which areas are likely to be requested in the future based on spatial cross-correlation between an unconstrained catalog of geographic features and a record of past cache requests. Tiles that are anticipated to be most frequently requested can be pre-generated and cached for faster retrieval. Trace-driven simulations with several million cache requests from two different nation-wide public web map services in Spain demonstrate that accurate predictions and performance gains can be obtained with the proposed model.
ISSN:1365-8816
1365-8824
1362-3087
DOI:10.1080/13658816.2012.721555