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首页> 外文会议>Twenty-Seventh International Conference on Very Large Data Bases, 27th, Sep 11-14th, 2001, Roma, Italy >Update Propagation Strategies for Improving the Quality of Data on the Web
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Update Propagation Strategies for Improving the Quality of Data on the Web

机译：更新传播策略以提高Web上数据的质量

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摘要

Dynamically generated web pages are ubiquitous today but their high demand for resources creates a huge scalability problem at the servers. Traditional web caching is not able to solve this problem since it cannot provide any guarantees as to the freshness of the cached data. A robust solution to the problem is web materialization, where pages are cached at the web server and constantly updated in the background, resulting in fresh data accesses on cache hits. In this work, we define Quality of Data metrics to evaluate how fresh the data served to the users is. We then focus on the update scheduling problem: given a set of views that are materialized, find the best order to refresh them, in the presence of continuous updates, so that the overall Quality of Data (QoD) is maximized. We present a QoD-aware Update Scheduling algorithm that is adaptive and tolerant to surges in the incoming update stream. We performed extensive experiments using real traces and synthetic ones, which show that our algorithm consistently outperforms FIFO scheduling by up to two orders of magnitude.
机译：如今，动态生成的网页无处不在，但是对资源的高需求在服务器上造成了巨大的可伸缩性问题。传统的Web缓存无法解决此问题，因为它无法提供有关缓存数据新鲜度的任何保证。解决该问题的可靠方法是Web实现，在Web实现中将页面缓存在Web服务器上，并在后台不断更新，从而在缓存命中时进行新的数据访问。在这项工作中，我们定义了数据质量指标以评估提供给用户的数据有多新鲜。然后，我们将重点放在更新调度问题上：给定已实现的视图集，在存在连续更新的情况下找到刷新它们的最佳顺序，从而使整体数据质量（QoD）最大化。我们提出了一种QoD感知的更新调度算法，该算法具有适应性并能容忍传入更新流中的浪涌。我们使用真实迹线和合成迹线进行了广泛的实验，这表明我们的算法始终比FIFO调度性能高出两个数量级。

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