You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Nowadays, more and more people use the Web as their primary source of up-to-date information. In this context, fast crawling and indexing of newly created Web pages has become crucial for search engines, especially because user traffic to a significant fraction of these new pages (like news, blog and forum posts) grows really quickly right after they appear, but lasts only for several days. In this paper, we study the problem of timely finding and crawling of such ephemeral new pages (in terms of user interest). Traditional crawling policies do not give any particular priority to such pages and may thus crawl them not quickly enough, and even crawl already obsolete content. We thus propose a new metric, well thought out for this task, which takes into account the decrease of user interest for ephemeral pages over time. We show that most ephemeral new pages can be found at a relatively small set of content sources and present a procedure for finding such a set. Our idea is to periodically recrawl content sources and crawl newly created pages linked from them, focusing on high-quality (in terms of user interest) content. One of the main difficulties here is to divide resources between these two activities in an efficient way. We find the adaptive balance between crawls and recrawls by maximizing the proposed metric. Further, we incorporate search engine click logs to give our crawler an insight about the current user demands. Efficiency of our approach is finally demonstrated experimentally on real-world data.

Cited Abstract(s):
[1]: Many online data sources are updated autonomously and independently. In this article, we make the case for estimating the change frequency of data to improve Web crawlers, Web caches and to help data mining. We first identify various scenarios, where different applications have different requirements on the accuracy of the estimated frequency. Then we develop several "frequency estimators" for the identified scenarios, showing analytically and experimentally how precise they are. In many cases, our proposed estimators predict change frequencies much more accurately and improve the effectiveness of applications. For example, a Web crawler could achieve 35p improvement in "freshness" simply by adopting our proposed estimator.

[2]: 

[3]: Accurate prediction of changing web page content improves a variety of retrieval and web related components. For example, given such a prediction algorithm one can both design a better crawling strategy that only recrawls pages when necessary as well as a proactive mechanism for personalization that pushes content associated with user revisitation directly to the user. While many techniques for modeling change have focused simply on past change frequency, our work goes beyond that by additionally studying the usefulness in page change prediction of: the page's content; the degree and relationship among the prediction page's observed changes; the relatedness to other pages and the similarity in the types of changes they undergo. We present an expert prediction framework that incorporates the information from these other signals more effectively than standard ensemble or basic relational learning techniques. In an empirical analysis, we find that using page content as well as related pages significantly improves prediction accuracy and compare it to common approaches. We present numerous similarity metrics to identify related pages and focus specifically on measures of temporal content similarity. We observe that the different metrics yield related pages that are qualitatively different in nature and have different effects on the prediction performance.

[4]: 

[5]: Most information depreciates over time, so keeping Web pages current presents new design challenges. This article quantifies what "current" means for Web search engines and estimates how often they must reindex the Web to keep current with its changing pages and structure.

[6]: In this paper we study how to refresh a local copy of an autonomous data source to maintain the copy up-to-date. As the size of the data grows, it becomes more difficult to maintain the copy fresh, “making it crucial to synchronize the copy effectively. We define two freshness metrics, change models of the underlying data, and synchronization policies. We analytically study how effective the various policies are. We also experimentally verify our analysis, based on data collected from 270 web sites for more than 4 months, and we show that our new policy improves the freshness” very significantly compared to current policies in use.

[7]: Abstract Recent experiments and analysis suggest that there are about 800 million publicly-indexable Web pages. However, unlike books in a traditional library, Web pages continue to change even after they are initially published by their authors and indexed by search engines. This paper describes preliminary data on and statistical analysis of the frequency and nature of Web page modifications. Using empirical models and a novel analytic metric of up-to-dateness', we estimate the rate at which Web search engines must re-index the Web to remain current.
The purpose of refresh policies is to recrawl known pages that have changed in order to keep a search engine's index fresh. Usually, such policies are based on some model, which predicts changes on Web pages. In pioneering works [7] [5] [4] [1] [6] , the analysis of pages' changes was made in the assumption of a time-homogeneous Poisson process, i.e., it was assumed that the pages change rate does not depend on time. However, in [7] , it was noted that there are daily and weekly trends in the pages change rate. Then, a history-based estimator, which takes such trends into account, was proposed in [2] . A more sophisticated approach based on machine learning is used in [3] , where the page's content, the degree of observed changes and other features are taken into account.