Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamically generated b pages are ubiquitous today but their high demand for resources creates a huge scalability problem at the servers. Traditional b 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 b materialization, where pages are cached at the b server and constantly updated in the background, resulting in fresh data accesses on cache hits. In this work,  define Quality of Data metrics to evaluate how fresh the data served to the users is.  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.  present a QoD-aware Update Scheduling algorithm that is adaptive and tolerant to surges in the incoming update stream.  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.
['Alexandros Labrinidis', 'Nick Roussopoulos']
I am writing a research paper on a idea Update Propagation Strategies for Improving the Quality of Data on the Web
[{"from": "human", "value": "I am writing a research paper on a idea Update Propagation Strategies for Improving the Quality of Data on the Web"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamically generated b pages are ubiquitous today but their high demand for resources creates a huge scalability problem at the servers. Traditional b 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 b materialization, where pages are cached at the b server and constantly updated in the background, resulting in fresh data accesses on cache hits. In this work,  define Quality of Data metrics to evaluate how fresh the data served to the users is.  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.  present a QoD-aware Update Scheduling algorithm that is adaptive and tolerant to surges in the incoming update stream.  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."}]