WEB CRAWLER

A web crawler (also known as a web spider, web robot, or—especially in the FOAF community—web scutter) is a program or automated script that browses the World Wide Web in a methodical, automated manner. Other less frequently used names for web crawlers are ants, automatic indexers, bots, and worms.
This process is called web crawling or spidering. Many sites, in particular search engines, use spidering as a means of providing up-to-date data. Web crawlers are mainly used to create a copy of all the visited pages for later processing by a search engine that will index the downloaded pages to provide fast searches. Crawlers can also be used for automating maintenance tasks on a website, such as checking links or validating HTML code. Also, crawlers can be used to gather specific types of information from Web pages, such as harvesting e-mail addresses (usually for spam).
A web crawler is one type of bot, or software agent. In general, it starts with a list of URLs to visit, called the seeds. As the crawler visits these URLs, it identifies all the hyperlinks in the page and adds them to the list of URLs to visit, called the crawl frontier. URLs from the frontier are recursively visited according to a set of policies.

Crawling policies



There are three important characteristics of the Web that make crawling it very difficult:
its large volume, its fast rate of change, dynamic page generation, which combine to produce a wide variety of possible crawlable URLs.
The large volume implies that the crawler can only download a fraction of the web pages within a given time, so it needs to prioritize its downloads. The high rate of change implies that by the time the crawler is downloading the last pages from a site, it is very likely that new pages have been added to the site, or that pages have already been updated or even deleted.
The recent increase in the number of pages being generated by server-side scripting languages has also created difficulty in that endless combinations of HTTP GET parameters exist, only a small selection of which will actually return unique content. For example, a simple online photo gallery may offer three options to users, as specified through HTTP GET parameters. If there exist four ways to sort images, three choices of thumbnail size, two file formats, and an option to disable user-provided contents, then that same set of content can be accessed with forty-eight different URLs, all of which will be present on the site. This mathematical combination creates a problem for crawlers, as they must sort through endless combinations of relatively minor scripted changes in order to retrieve unique content.
As Edwards et al. noted, "Given that the bandwidth for conducting crawls is neither infinite nor free, it is becoming essential to crawl the Web in not only a scalable, but efficient way, if some reasonable measure of quality or freshness is to be maintained." A crawler must carefully choose at each step which pages to visit next.
The behavior of a web crawler is the outcome of a combination of policies:
A selection policy that states which pages to download. A re-visit policy that states when to check for changes to the pages. A politeness policy that states how to avoid overloading websites. A parallelization policy that states how to coordinate distributed web crawlers.
Selection policyGiven the current size of the Web, even large search engines cover only a portion of the publicly available internet; a study by Lawrence and Giles (Lawrence and Giles, 2000) showed that no search engine indexes more than 16% of the Web. As a crawler always downloads just a fraction of the Web pages, it is highly desirable that the downloaded fraction contains the most relevant pages, and not just a random sample of the Web.
This requires a metric of importance for prioritizing Web pages. The importance of a page is a function of its intrinsic quality, its popularity in terms of links or visits, and even of its URL (the latter is the case of vertical search engines restricted to a single top-level domain, or search engines restricted to a fixed Web site). Designing a good selection policy has an added difficulty: it must work with partial information, as the complete set of Web pages is not known during crawling.
Cho et al. (Cho et al., 1998) made the first study on policies for crawling scheduling. Their data set was a 180,000-pages crawl from the stanford.edu domain, in which a crawling simulation was done with different strategies. The ordering metrics tested were breadth-first, backlink-count and partial Pagerank calculations. One of the conclusions was that if the crawler wants to download pages with high Pagerank early during the crawling process, then the partial Pagerank strategy is the better, followed by breadth-first and backlink-count. However, these results are for just a single domain.
Najork and Wiener [4] performed an actual crawl on 328 million pages, using breadth-first ordering. They found that a breadth-first crawl captures pages with high Pagerank early in the crawl (but they did not compare this strategy against other strategies). The explanation given by the authors for this result is that "the most important pages have many links to them from numerous hosts, and those links will be found early, regardless of on which host or page the crawl originates".
Abiteboul (Abiteboul et al., 2003) designed a crawling strategy based on an algorithm called OPIC (On-line Page Importance Computation). In OPIC, each page is given an initial sum of "cash" that is distributed equally among the pages it points to. It is similar to a Pagerank computation, but it is faster and is only done in one step. An OPIC-driven crawler downloads first the pages in the crawling frontier with higher amounts of "cash". Experiments were carried in a 100,000-pages synthetic graph with a power-law distribution of in-links. However, there was no comparison with other strategies nor experiments in the real Web.
Boldi et al. (Boldi et al., 2004) used simulation on subsets of the Web of 40 million pages from the .it domain and 100 million pages from the WebBase crawl, testing breadth-first against depth-first, random ordering and an omniscient strategy. The comparison was based on how well PageRank computed on a partial crawl approximates the true PageRank value. Surprisingly, some visits that accumulate PageRank very quickly (most notably, breadth-first and the omniscent visit) provide very poor progressive approximations.
Baeza-Yates et al. [5] used simulation on two subsets of the Web of 3 million pages from the .gr and .cl domain, testing several crawling strategies. They showed that both the OPIC strategy and a strategy that uses the length of the per-site queues are both better than breadth-first crawling, and that it is also very effective to use a previous crawl, when it is available, to guide the current one.
Daneshpajouh et al. [6] designed a community based algorithm for discovering good seeds. Their method crawls web pages with high PageRank from different communities in less iteration in comparison with crawl starting from random seeds. One can extract good seed from a previously crawled web graph using this new method. Using these seeds a new crawl can be very effective.
Restricting followed linksA crawler may only want to seek out HTML pages and avoid all other MIME types. In order to request only HTML resources, a crawler may make an HTTP HEAD request to determine a Web resource's MIME type before requesting the entire resource with a GET request. To avoid making numerous HEAD requests, a crawler may alternatively examine the URL and only request the resource if the URL ends with .html, .htm or a slash. This strategy may cause numerous HTML Web resources to be unintentionally skipped. A similar strategy compares the extension of the web resource to a list of known HTML-page types: .html, .htm, .asp, .aspx, .php, and a slash.
Some crawlers may also avoid requesting any resources that have a "?" in them (are dynamically produced) in order to avoid spider traps that may cause the crawler to download an infinite number of URLs from a Web site.
Path-ascending crawlingSome crawlers intend to download as many resources as possible from a particular Web site. Cothey (Cothey, 2004) introduced a path-ascending crawler that would ascend to every path in each URL that it intends to crawl. For example, when given a seed URL of http://llama.org/hamster/monkey/page.html, it will attempt to crawl /hamster/monkey/, /hamster/, and /. Cothey found that a path-ascending crawler was very effective in finding isolated resources, or resources for which no inbound link would have been found in regular crawling.
Many Path-ascending crawlers are also known as Harvester software, because they're used to "harvest" or collect all the content - perhaps the collection of photos in a gallery - from a specific page or host.
Focused crawlingMain article: Focused crawlerThe importance of a page for a crawler can also be expressed as a function of the similarity of a page to a given query. Web crawlers that attempt to download pages that are similar to each other are called focused crawler or topical crawlers. The concepts of topical and focused crawling were first introduced by Menczer [7] [8] and by Chakrabarti et al.
The main problem in focused crawling is that in the context of a web crawler, we would like to be able to predict the similarity of the text of a given page to the query before actually downloading the page. A possible predictor is the anchor text of links; this was the approach taken by Pinkerton in a crawler developed in the early days of the Web. Diligenti et al. [11] propose to use the complete content of the pages already visited to infer the similarity between the driving query and the pages that have not been visited yet. The performance of a focused crawling depends mostly on the richness of links in the specific topic being searched, and a focused crawling usually relies on a general Web search engine for providing starting points.