Crawling Techniques   

Google Search engines are answer machines. When a performs query, the search engine search of billions of documents and does two things: first, it returns only those results that are relevant  to the  query; second, it ranks those results according to the popularity of the websites. There are two crawling techniques.

 Focused Crawling


A general purpose Web crawler gathers as many pages as it can from a particular set of URL’s. Where as a focused crawler is designed to only gather documents on a specific topic, thus reducing the amount of network traffic and downloads. The goal of the focused crawler is to selectively seek out pages that are relevant to a pre-defined set of topics. The topics are specified not using keywords, but using exemplary documents. Rather than collecting and indexing all accessible web documents to be able to answer all possible ad-hoc queries, a focused crawler analyzes its crawl boundary to find the links that are likely to be most relevant for the crawl, and avoids irrelevant regions of the web. This leads to significant savings in hardware and network resources, and helps keep the crawl more up-to-date. The focused crawler has three main components: a classifier, which makes relevance judgments on pages crawled to decide on link expansion, a distiller which determines a measure of centrality of crawled pages to determine visit priorities, and a crawler with dynamically reconfigurable priority controls which is governed by the classifier and distiller. The most crucial evaluation of focused crawling is to measure the harvest ratio, which is rate at which relevant pages are acquired and irrelevant pages are effectively filtered off from the crawl. This harvest ratio must be high, otherwise the focused crawler would spend a lot of time merely eliminating irrelevant pages, and it may be better to use an ordinary crawler instead.

 Distributed Crawling


Indexing the web is a challenge due to its growing and dynamic nature. As the size of the Web is growing it has become imperative to parallelize the crawling process in order to finish downloading the pages in a reasonable amount of time. A single crawling process even if multithreading is used will be insufficient for large – scale engines that need to fetch large amounts of data rapidly. When a single centralized crawler is used all the fetched data passes through a single physical link. Distributing the crawling activity via multiple processes can help build a scalable, easily configurable system, which is fault tolerant system. Splitting the load decreases hardware requirements and at the same time increases the overall download speed and reliability. Each task is performed in a fully distributed fashion, that is, no central coordinator exists.

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