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首页> 外文期刊>International Journal of Geographical Information Science >An OLS regression model for context-aware tile prefetching in a web map cache
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An OLS regression model for context-aware tile prefetching in a web map cache

机译：用于Web地图缓存中的上下文感知图块预取的OLS回归模型

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摘要

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.
机译：Web地图服务的日益普及激发了空间数据基础架构中更多可伸缩服务的开发。平铺地图服务已经成为传统地图服务的可扩展替代方案。无需动态渲染地图图像，而是可以从服务器端缓存非常快速地提供一组预生成的图像图块。但是，在服务启动期间，缓存最初是空的，用户体验的服务质量很差。切片预取尝试通过主动获取地图图像而不等待客户端请求来提高命中率。尽管传统Web缓存中最流行的预取策略仅考虑以前的访问历史来进行预测，但通过考虑背景地理信息，可以在Web映射中实现重大改进。这项工作提出了一种回归模型，以基于不受约束的地理特征目录和过去的缓存请求记录之间的空间互相关性来预测将来可能会请求哪些区域。可以预生成预期要最频繁请求的图块并对其进行缓存，以加快检索速度。来自西班牙两个不同的全国性公共网络地图服务的数百万个缓存请求的跟踪驱动模拟表明，使用所提出的模型可以获得准确的预测和性能提升。

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