In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm. The task of the crawlers is to find new information on the Web. The selection algorithm, called weblog update, modifies the starting URL lists of our crawlers based on the found URLs containing new information. The reinforcement learning algorithm modifies the URL orderings of the crawlers based on the received reinforcements for submitted documents. We performed simulations based on data collected from the Web. The collected portion of the Web is typical and exhibits scale-free small world (SFSW) structure. We have found that on this SFSW, the weblog update algorithm performs better than the reinforcement learning algorithm. It finds the new information faster than the reinforcement learning algorithm and has better new information all submitted documents ratio. We believe that the advantages of the selection algorithm over reinforcement learning algorithm is due to the small world property of the Web.
Risvik and Michelsen @cite_0 mention that because of the exponential growth of the Web there is an ever increasing need for more intelligent, (topic-)specific algorithms for crawling, like focused crawling and document classification. With these algorithms crawlers and search engines can operate more efficiently in a topically limited document space. The authors also state that in such vertical regions the dynamics of the Web pages is more homogenous.
@cite_0
2110073539 2100612254 2135402295 2046862025
This paper presents a comparative study of strategies for Web crawling. We show that a combination of breadth-first ordering with the largest sites first is a practical alternative since it is fast, simple to implement, and able to retrieve the best ranked pages at a rate that is closer to the optimal than other alternatives. Our study was performed on a large sample of the Chilean Web which was crawled by using simulators, so that all strategies were compared under the same conditions, and actual crawls to validate our conclusions. We also explored the effects of large scale parallelism in the page retrieval task and multiple-page requests in a single connection for effective amortization of latency times. Some large scale topical digital libraries, such as CiteSeer, harvest online academic documents by crawling open-access archives, university and author homepages, and authors' self-submissions. While these approaches have so far built reasonable size libraries, they can suffer from having only a portion of the documents from specific publishing venues. We propose to use alternative online resources and techniques that maximally exploit other resources to build the complete document collection of any given publication venue. We investigate the feasibility of using publication metadata to guide the crawler towards authors' homepages to harvest what is missing from a digital library collection. We collect a real-world dataset from two Computer Science publishing venues, involving a total of 593 unique authors over a time frame of 1998 to 2004. We then identify the missing papers that are not indexed by CiteSeer. Using a fully automatic heuristic-based system that has the capability of locating authors' homepages and then using focused crawling to download the desired papers, we demonstrate that it is practical to harvest using a focused crawler academic papers that are missing from our digital library. Our harvester achieves a performance with an average recall level of 0.82 overall and 0.75 for those missing documents. Evaluation of the crawler's performance based on the harvest rate shows definite advantages over other crawling approaches and consistently outperforms a defined baseline crawler on a number of measures Web archives preserve the history of born-digital content and offer great potential for sociologists, business analysts, and legal experts on intellectual property and compliance issues. Data quality is crucial for these purposes. Ideally, crawlers should gather sharp captures of entire Web sites, but the politeness etiquette and completeness requirement mandate very slow, long-duration crawling while Web sites undergo changes. This paper presents the SHARC framework for assessing the data quality in Web archives and for tuning capturing strategies towards better quality with given resources. We define quality measures, characterize their properties, and derive a suite of quality-conscious scheduling strategies for archive crawling. It is assumed that change rates of Web pages can be statistically predicted based on page types, directory depths, and URL names. We develop a stochastically optimal crawl algorithm for the offline case where all change rates are known. We generalize the approach into an online algorithm that detect information on a Web site while it is crawled. For dating a site capture and for assessing its quality, we propose several strategies that revisit pages after their initial downloads in a judiciously chosen order. All strategies are fully implemented in a testbed, and shown to be effective by experiments with both synthetically generated sites and a daily crawl series for a medium-sized site. Abstract In this paper we study several dimensions of Web dynamics in the context of large-scale Internet search engines. Both growth and update dynamics clearly represent big challenges for search engines. We show how the problems arise in all components of a reference search engine model. Furthermore, we use the FAST Search Engine architecture as a case study for showing some possible solutions for Web dynamics and search engines. The focus is to demonstrate solutions that work in practice for real systems. The service is running live at www.alltheweb.com and major portals worldwide with more than 30 million queries a day, about 700 million full-text documents, a crawl base of 1.8 billion documents, updated every 11 days, at a rate of 400 documents second. We discuss future evolution of the Web, and some important issues for search engines will be scheduling and query execution as well as increasingly heterogeneous architectures to handle the dynamic Web.