Visualizza wikitesto di Web crawler

{{citation style|date=December 2007|article|For example, citations normally are placed immediately after a punctuation mark, such as after a comma, colon, or the period at the end of a sentence.}}
{{Cleanup-jargon|date=June 2008}}

{{For|the [[Web search engine|search engine]] of the same name|WebCrawler}}
{{For|the fictional robots called Scutters|Red Dwarf characters#The Skutters}}

A '''Web crawler''' (also known as a '''Web spider''', '''Web robot''', or—especially in the [[FOAF (software)|FOAF]] community—'''Web scutter'''<ref>See [http://wiki.foaf-project.org/Scutter definition of scutter on FOAF Project's wiki]</ref>) 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'''.<ref> {{cite journal | author = Kobayashi, M. and Takeda, K. | title = Information retrieval on the web | journal = ACM Computing Surveys | volume = 32 | issue = 2| pages = 144–173 | year = 2000 | publisher = ACM Press | url = http://doi.acm.org/10.1145/358923.358934 | doi = 10.1145/358923.358934 }}</ref>

This process is called '''Web crawling''' or '''spidering'''. Many sites, in particular [[Web search engine|search engine]]s, 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 (search engine)|index]] the downloaded pages to provide fast searches. Crawlers can also be used for automating maintenance tasks on a Web site, 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 [[spamming|spam]]).

A Web crawler is one type of [[Internet bot|bot]], or software agent. In general, it starts with a list of [[Uniform Resource Locator|URL]]s to visit, called the '''seeds'''. As the crawler visits these URLs, it identifies all the [[hyperlink]]s 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, and
* dynamic page generation.

These characteristics combine to produce a wide variety of possible crawlable [[URL]]s.

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|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 (computing)|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." <ref name=edwards2001>{{cite journal|author = Edwards, J., McCurley, K. S., and Tomlin, J. A. | title = [http://www10.org/cdrom/papers/210/index.html  An adaptive model for optimizing performance of an incremental web crawler] | journal = In Proceedings of the Tenth Conference on World Wide Web | pages = 106–113 | location = Hong Kong | year = 2001 | publisher = Elsevier Science | doi = 10.1145/371920.371960 }}</ref>. 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 Web sites, and
* a ''parallelization policy'' that states how to coordinate distributed Web crawlers.

=== Selection policy ===

Given the current size of the Web, even large search engines cover only a portion of the publicly-available Internet; a study by [[Dr. Steve Lawrence|Lawrence]] and [[Lee Giles|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|vertical search engine]]s 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 <tt>stanford.edu</tt> domain, in which a crawling simulation was done with different strategies. The ordering metrics tested were [[Breadth-first search|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 search|breadth-first]] and backlink-count. However, these results are for just a single domain.
 
Najork and Wiener <ref>Marc Najork and Janet L. Wiener. [http://www10.org/cdrom/papers/pdf/p208.pdf Breadth-first crawling yields high-quality pages]. In Proceedings of the Tenth Conference on World Wide Web, pages 114–118, Hong Kong, May 2001. Elsevier Science.</ref> performed an actual crawl on 328 million pages, using [[Breadth-first search|breadth-first]] ordering. They found that a [[Breadth-first search|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 <tt>.it</tt> domain and 100 million pages from the WebBase crawl, testing [[Breadth-first search|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 search|breadth-first]] and the omniscent visit) provide very poor progressive approximations.

Baeza-Yates ''et al.'' <ref name=baeza2005>Baeza-Yates, R., Castillo, C., Marin, M. and Rodriguez, A. (2005). [http://www.dcc.uchile.cl/%7Eccastill/papers/baeza05_crawling_country_better_breadth_first_web_page_ordering.pdf Crawling a Country: Better Strategies than Breadth-First for Web Page Ordering]. In Proceedings of the Industrial and Practical Experience track of the 14th conference on World Wide Web, pages 864–872, Chiba, Japan. ACM Press.</ref> used simulation on two subsets of the Web of 3 million pages from the <tt>.gr</tt> and <tt>.cl</tt> 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 better than [[Breadth-first search|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.'' <ref>Shervin Daneshpajouh, Mojtaba Mohammadi Nasiri, Mohammad Ghodsi, A Fast Community Based Algorithm for Generating Crawler Seeds Set, To be appear in proceeding of 4th International Conference on Web Information Systems and Technologies ([http://www.webist.org/ WEBIST-2008]), Funchal, Portugal, May 2008.</ref> 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 links====
A crawler may only want to seek out HTML pages and avoid all other [[MIME type]]s. 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 [[Query string|"?"]] in them (are dynamically produced) in order to avoid [[spider trap]]s that may cause the crawler to download an infinite number of URLs from a Web site.

====Path-ascending crawling====
Some 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 <nowiki>http://llama.org/hamster/monkey/page.html</nowiki>, 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 crawling====
{{main|Focused crawler}}
The 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 <ref>Menczer, F. (1997). [http://informatics.indiana.edu/fil/Papers/ICML.ps ARACHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods for Information Discovery]. In D. Fisher, ed., Machine Learning: Proceedings of the 14th International Conference (ICML97). Morgan Kaufmann</ref> <ref>Menczer, F. and Belew, R.K. (1998). [http://informatics.indiana.edu/fil/Papers/AA98.ps Adaptive Information Agents in Distributed Textual Environments]. In K. Sycara and M. Wooldridge (eds.) Proc. 2nd Intl. Conf. on Autonomous Agents (Agents '98). ACM Press</ref> and by Chakrabarti ''et al.'' <ref>Chakrabarti, S., van den Berg, M., and Dom, B. (1999). [http://web.archive.org/web/20040317210216/http://www.fxpal.com/people/vdberg/pubs/www8/www1999f.pdf Focused crawling: a new approach to topic-specific web resource discovery]. Computer Networks, 31(11–16):1623–1640.</ref>.

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 <ref name=pinkerton1994>Pinkerton, B. (1994). [http://web.archive.org/web/20010904075500/http://archive.ncsa.uiuc.edu/SDG/IT94/Proceedings/Searching/pinkerton/WebCrawler.html Finding what people want: Experiences with the WebCrawler]. In Proceedings of the First World Wide Web Conference, Geneva, Switzerland.</ref> in a crawler developed in the early days of the Web. Diligenti ''et al.'' <ref>Diligenti, M., Coetzee, F., Lawrence, S., Giles, C. L., and Gori, M. (2000). [http://nautilus.dii.unisi.it/pubblicazioni/files/conference/2000-Diligenti-VLDB.pdf Focused crawling using context graphs]. In Proceedings of 26th International Conference on Very Large Databases (VLDB), pages 527-534, Cairo, Egypt.</ref> 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.

====Crawling the Deep Web====
A vast amount of Web pages lie in the [[deep web|deep]] or invisible Web. These pages are typically only accessible by submitting queries to a database, and regular crawlers are unable to find these pages if there are no links that point to them. Google’s [[Google Sitemap|Sitemap Protocol]] and [[mod oai]] (Nelson ''et al.'', 2005) are intended to allow discovery of these deep-Web resources.

Deep Web crawling also multiplies the number of Web links to be crawled. Some crawlers only take some of the <a href="URL" shaped URLS. In some cases, such as the [[Googlebot]], Web crawling is done on [http://www.netdynamics.eu/analytics/text-scannings-from-google-bot.html all text] contained inside the hypertext content, tags, or text.

====Web 3.0 Crawling====

[[Web 3.0]] defines advanced technologies and new principles for the next generation [[Search engine technology|search technologies]] that is summarized in [[Semantic Web]] and [[Website Parse Template]] concepts for the present. [[ICDL crawling|Web 3.0 crawling]] and [[Web indexing|indexing]] technologies will be based on human-machine clever associations.

=== Re-visit policy ===

The Web has a very dynamic nature, and crawling a fraction of the Web can take a really long time, usually measured in weeks or months. By the time a Web crawler has finished its crawl, many events could have happened. These events can include creations, updates and deletions.

From the search engine's point of view, there is a cost associated with not detecting an event, and thus having an outdated copy of a resource. The most-used cost functions, introduced in (Cho and Garcia-Molina, 2000), are freshness and age.

'''Freshness''': This is a binary measure that indicates whether the local copy is accurate or not. The freshness of a page ''p'' in the repository at time ''t'' is defined as:

: <math>
F_p(t) = \begin{cases} 1 & {\rm
if}~p~{\rm~is~equal~to~the~local~copy~at~time}~t\\ 0 & {\rm otherwise} \end{cases}
</math>

'''Age''': This is a measure that indicates how outdated the local copy is. The age of a page ''p'' in the repository, at time ''t'' is defined as:

: <math>
A_p(t) =  \begin{cases} 0  & {\rm if}~p~{\rm~is~not~modified~at~time}~t\\ t - {\rm modification~time~of}~p &
{\rm otherwise} \end{cases} 
</math>

Coffman ''et al.'' (Edward G. Coffman, 1998) worked with a definition of the objective of a Web crawler that is equivalent to freshness, but use a different wording: they propose that a crawler must minimize the fraction of time pages remain outdated. They also noted that the problem of Web crawling can be modeled as a multiple-queue, single-server polling system, on which the Web crawler is the server and the Web sites are the queues. Page modifications are the arrival of the customers, and switch-over times are the interval between page accesses to a single Web site. Under this model, mean waiting time for a customer in the polling system is equivalent to the average age for the Web crawler.

The objective of the crawler is to keep the average freshness of pages in its collection as high as possible, or to keep the average age of pages as low as possible. These objectives are not equivalent: in the first case, the crawler is just concerned with how many pages are out-dated, while in the second case, the crawler is concerned with how old the local copies of pages are.

Two simple re-visiting policies were studied by Cho and Garcia-Molina <ref name=cho2003>Cho, J. and Garcia-Molina, H. (2003). [http://portal.acm.org/citation.cfm?doid=958942.958945 Effective page refresh policies for web crawlers]. ACM Transactions on Database Systems, 28(4).</ref>:

'''Uniform policy''': This involves re-visiting all pages in the collection with the same frequency, regardless of their rates of change.

'''Proportional policy''': This involves re-visiting more often the pages that change more frequently. The visiting frequency is directly proportional to the (estimated) change frequency.

(In both cases, the repeated crawling order of pages can be done either in a random or a fixed order.) 

Cho and Garcia-Molina proved the surprising result that, in terms of average freshness, the uniform policy outperforms the proportional policy in both a simulated Web and a real Web crawl. The explanation for this result comes from the fact that, when a page changes too often, the crawler will waste time by trying to re-crawl it too fast and still will not be able to keep its copy of the page fresh.

To improve freshness, we should penalize the elements that change too often (Cho and Garcia-Molina, 2003a). The optimal re-visiting policy is neither the uniform policy nor the proportional policy. The optimal method for keeping average freshness high includes ignoring the pages that change too often, and the optimal for keeping average age low is to use access frequencies that monotonically (and sub-linearly) increase with the rate of change of each page. In both cases, the optimal is closer to the uniform policy than to the proportional policy: as Coffman ''et al.'' (Edward G. Coffman, 1998) note, "in order to minimize the expected obsolescence time, the accesses to any particular page should be kept as evenly spaced as possible". Explicit formulas for the re-visit policy are not attainable in general, but they are obtained numerically, as they depend on the distribution of page changes. (Cho and Garcia-Molina, 2003a) show that the exponential distribution is a good fit for describing page changes, while Ipeirotis ''et al.'' <ref>Ipeirotis, P., Ntoulas, A., Cho, J., Gravano, L. (2005) [http://pages.stern.nyu.edu/~panos/publications/icde2005.pdf Modeling and managing content changes in text databases]. In Proceedings of the 21st IEEE International Conference on Data Engineering, pages 606-617, April 2005, Tokyo.</ref> show how to use statistical tools to discover parameters that affect this distribution. Note that the re-visiting policies considered here regard all pages as homogeneous in terms of quality ("all pages on the Web are worth the same"), something that is not a realistic scenario, so further information about the Web page quality should be included to achieve a better crawling policy.

=== Politeness policy ===

Crawlers can retrieve data much quicker and in greater depth than human searchers, so they can have a crippling impact on the performance of a site. Needless to say, if a single crawler is performing multiple requests per second and/or downloading large files, a server would have a hard time keeping up with requests from multiple crawlers.

As noted by Koster <ref>Koster, M. (1995). Robots in the web: threat or treat ? ConneXions, 9(4).</ref>, the use of Web crawlers is useful for a number of tasks, but comes with a price for the general community. The costs of using Web crawlers include:
* network resources, as crawlers require considerable bandwidth and operate with a high degree of parallelism during a long period of time;
* server overload, especially if the frequency of accesses to a given server is too high;
* poorly-written crawlers, which can crash servers or routers, or which download pages they cannot handle; and
* personal crawlers that, if deployed by too many users, can disrupt networks and Web servers.

A partial solution to these problems is the [[Robots Exclusion Standard|robots exclusion protocol]], also known as the robots.txt protocol <ref>Koster, M. (1996). [http://www.robotstxt.org/wc/exclusion.html A standard for robot exclusion].</ref> that is a standard for administrators to indicate which parts of their Web servers should not be accessed by crawlers. This standard does not include a suggestion for the interval of visits to the same server, even though this interval is the most effective way of avoiding server overload. Recently commercial search engines like [[Ask Jeeves]], [[MSN]] and [[Yahoo!|Yahoo]] are able to use an extra "Crawl-delay:" parameter in the robots.txt file to indicate the number of seconds to delay between requests.

The first proposal for the interval between connections was given in <ref>Koster, M. (1993). [http://www.robotstxt.org/wc/guidelines.html Guidelines for robots writers].</ref> and was 60 seconds. However, if pages were downloaded at this rate from a website with more than 100,000 pages over a perfect connection with zero latency and infinite bandwidth, it would take more than 2 months to download only that entire Web site; also, only a fraction of the resources from that Web server would be used. This does not seem acceptable.

Cho<ref name=cho2003/> uses 10 seconds as an interval for accesses, and the WIRE crawler <ref name=baeza2002>Baeza-Yates, R. and Castillo, C. (2002). [http://www.chato.cl/papers/baeza02balancing.pdf Balancing volume, quality and freshness in Web crawling]. In Soft Computing Systems – Design, Management and Applications, pages 565–572, Santiago, Chile. IOS Press Amsterdam.</ref> uses 15 seconds as the default. The MercatorWeb crawler (Heydon and Najork, 1999) follows an adaptive politeness policy: if it took ''t'' seconds to download a document from a given server, the crawler waits for 10''t'' seconds before downloading the next page. Dill ''et al.'' <ref>Dill, S., Kumar, R., Mccurley, K. S., Rajagopalan, S., Sivakumar, D., and Tomkins, A. (2002). [http://www.mccurley.org/papers/fractal.pdf Self-similarity in the web]. ACM Trans. Inter. Tech., 2(3):205–223.</ref> use 1 second.

For those using Web crawlers for research purposes, a more detailed cost-benefit analysis is needed and ethical considerations should be taken into account when deciding where to crawl and how fast to crawl <ref>{{cite web | coauthors = Thelwall, M. & Stuart, D . | year = 2006 | url = http://www.scit.wlv.ac.uk/%7Ecm1993/papers/Web_Crawling_Ethics_preprint.doc | title = Web crawling ethics revisited: Cost, privacy and denial of service | work = Journal of the American Society for Information Science and Technology}}</ref>.

Anecdotal evidence from access logs shows that access intervals from known crawlers vary between 20 seconds and 3–4 minutes. It is worth noticing that even when being very polite, and taking all the safeguards to avoid overloading Web servers, some complaints from Web server administrators are received. [[Sergey Brin|Brin]] and [[Larry Page|Page]] note that: "... running a crawler which connects to more than half a million servers (...) generates a fair amount of e-mail and phone calls. Because of the vast number of people coming on line, there are always those who do not know what a crawler is, because this is the first one they have seen." <ref name=brin1998>Brin, S. and Page, L. (1998). [http://infolab.stanford.edu/~backrub/google.html The anatomy of a large-scale hypertextual Web search engine]. Computer Networks and ISDN Systems, 30(1-7):107–117.</ref>.

=== Parallelization policy ===
{{main|Distributed web crawling}}

A [[Parallel computing|parallel]] crawler is a crawler that runs multiple processes in parallel. The goal is to maximize the download rate while minimizing the overhead from parallelization and to avoid repeated downloads of the same page. To avoid downloading the same page more than once, the crawling system requires a policy for assigning the new URLs discovered during the crawling process, as the same URL can be found by two different crawling processes.

==Web crawler architectures==
[[Image:WebCrawlerArchitecture.svg|frame|right|High-level architecture of a standard Web crawler]]
A crawler must not only have a good crawling strategy, as noted in the previous sections, but it should also have a highly optimized architecture.
 
Shkapenyuk and Suel <ref name=shkapenyuk2002>Shkapenyuk, V. and Suel, T. (2002). [http://cis.poly.edu/tr/tr-cis-2001-03.pdf Design and implementation of a high performance distributed web crawler]. In Proceedings of the 18th International Conference on Data Engineering (ICDE), pages 357-368, San Jose, California. IEEE CS Press.</ref> noted that: "While it is fairly easy to build a slow crawler that downloads a few pages per second for a short period of time, building a high-performance system that can download hundreds of millions of pages over several weeks presents a number of challenges in system design, I/O and network efficiency, and robustness and manageability."

Web crawlers are a central part of search engines, and details on their algorithms and architecture are kept as business secrets. When crawler designs are published, there is often an important lack of detail that prevents others from reproducing the work. There are also emerging concerns about "[[Spamdexing|search engine spamming]]", which prevent major search engines from publishing their ranking algorithms.
<br clear="all">

===URL normalization===

Crawlers usually perform some type of [[URL normalization]] in order to avoid crawling the same resource more than once. The term ''URL normalization'', also called ''URL canonicalization'', refers to the process of modifying and standardizing a URL in a consistent manner. There are several types of normalization that may be performed including conversion of URLs to lowercase, removal of "." and ".." segments, and adding trailing slashes to the non-empty path component (Pant ''et al.'', 2004).

==Crawler identification==

Web crawlers typically identify themselves to a Web server by using the [[user agent|User-agent]] field of an [[HTTP]] request. Web site administrators typically examine their [[Web server]]s’ log and use the user agent field to determine which crawlers have visited the web server and how often. The user agent field may include a [[Uniform Resource Locator|URL]] where the Web site administrator may find out more information about the crawler. [[Spambots]] and other malicious Web crawlers are unlikely to place identifying information in the user agent field, or they may mask their identity as a browser or other well-known crawler.

It is important for Web crawlers to identify themselves so that Web site administrators can contact the owner if needed. In some cases, crawlers may be accidentally trapped in a [[spider trap|crawler trap]] or they may be overloading a Web server with requests, and the owner needs to stop the crawler. Identification is also useful for administrators that are interested in knowing when they may expect their Web pages to be indexed by a particular [[Web search engine|search engine]].

== Examples of Web crawlers ==
The following is a list of published crawler architectures for general-purpose crawlers (excluding focused web crawlers), with a brief description that includes the names given to the different components and outstanding features:

* '''RBSE''' <ref>Eichmann, D. (1994). [http://mingo.info-science.uiowa.edu/eichmann/www94/Spider.ps The RBSE spider: balancing effective search against Web load]. In Proceedings of the First World Wide Web Conference, Geneva, Switzerland.</ref> was the first published web crawler. It was based on two programs: the first program, "<tt>spider</tt>" maintains a queue in a relational database, and the second program "<tt>mite</tt>", is a modified <tt>www</tt> [[ASCII]] browser that downloads the pages from the Web.
* '''WebCrawler''' <ref name=pinkerton1994/> was used to build the first publicly-available full-text index of a subset of the Web. It was based on lib-WWW to download pages, and another program to parse and order URLs for breadth-first exploration of the Web graph. It also included a real-time crawler that followed links based on the similarity of the anchor text with the provided query.
* '''World Wide Web Worm''' <ref>McBryan, O. A. (1994). GENVL and WWWW: Tools for taming the web. In Proceedings of the First World Wide Web Conference, Geneva, Switzerland.</ref> was a crawler used to build a simple index of document titles and URLs. The index could be searched by using the <tt>[[grep]]</tt> [[Unix]] command.
* '''[[Google]] Crawler''' <ref name=brin1998/> is described in some detail, but the reference is only about an early version of its architecture, which was based in C++ and [[Python (programming language)|Python]]. The crawler was integrated with the indexing process, because text parsing was done for full-text indexing and also for URL extraction. There is a URL server that sends lists of URLs to be fetched by several crawling processes. During parsing, the URLs found were passed to a URL server that checked if the URL have been previously seen. If not, the URL was added to the queue of the URL server.
* '''[[WebFountain]]''' <ref name=edwards2001/> is a distributed, modular crawler similar to Mercator but written in C++. It features a "controller" machine that coordinates a series of "ant" machines. After repeatedly downloading pages, a change rate is inferred for each page and a non-linear programming method must be used to solve the equation system for maximizing freshness. The authors recommend to use this crawling order in the early stages of the crawl, and then switch to a uniform crawling order, in which all pages are being visited with the same frequency.
* '''PolyBot''' <ref name=shkapenyuk2002/> is a distributed crawler written in C++ and Python, which is composed of a "crawl manager", one or more "downloaders" and one or more "DNS resolvers". Collected URLs are added to a queue on disk, and processed later to search for seen URLs in batch mode. The politeness policy considers both third and second level domains (e.g.: www.example.com and www2.example.com are third level domains) because third level domains are usually hosted by the same Web server.
* '''WebRACE''' <ref>Zeinalipour-Yazti, D. and Dikaiakos, M. D. (2002). [http://www.cs.ucr.edu/~csyiazti/downloads/papers/ngits02/ngits02.pdf Design and implementation of a distributed crawler and filtering processor]. In Proceedings of the Fifth Next Generation Information Technologies and Systems (NGITS), volume 2382 of Lecture Notes in Computer Science, pages 58–74, Caesarea, Israel. Springer.</ref> is a crawling and caching module implemented in Java, and used as a part of a more generic system called eRACE. The system receives requests from users for downloading web pages, so the crawler acts in part as a smart proxy server. The system also handles requests for "subscriptions" to Web pages that must be monitored: when the pages change, they must be downloaded by the crawler and the subscriber must be notified. The most outstanding feature of WebRACE is that, while most crawlers start with a set of "seed" URLs, WebRACE is continuously receiving new starting URLs to crawl from.
* '''FAST Crawler''' <ref>Risvik, K. M. and Michelsen, R. (2002). [http://citeseer.ist.psu.edu/rd/1549722%2C509701%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/26004/http:zSzzSzwww.idi.ntnu.nozSz%7EalgkonzSzgenereltzSzse-dynamicweb1.pdf/risvik02search.pdf Search Engines and Web Dynamics]. Computer Networks, vol. 39, pp. 289–302, June 2002.</ref> is a distributed crawler, used by [[Fast Search & Transfer]], and a general description of its architecture is available.{{Fact|date=January 2008}}

In addition to the specific crawler architectures listed above, there are general crawler architectures published by Cho (Cho and Garcia-Molina, 2002) and Chakrabarti <ref>Chakrabarti, S. (2003). [http://www.cs.berkeley.edu/~soumen/mining-the-web/ Mining the Web]. Morgan Kaufmann Publishers. ISBN 1-55860-754-4</ref>.

=== Open-source crawlers ===
* '''[[DataparkSearch]]''' is a crawler and search engine released under the [[GNU General Public License]].
* '''[[Wget|GNU Wget]]''' is a [[Command line interface|command-line]]-operated crawler written in [[C (programming language)|C]] and released under the [[GNU General Public License|GPL]]. It is typically used to mirror Web and FTP sites.
* '''[[Grub_(search_engine)|GRUB]]''' is an open source distributed search crawler that Wikia Search ( http://wikiasearch.com ) uses to crawl the web.
* '''[[Heritrix]]''' is the [[Internet Archive]]'s archival-quality crawler, designed for archiving periodic snapshots of a large portion of the Web. It was written in [[Java (programming language)|Java]].
* '''[[Ht-//dig|ht://Dig]]''' includes a Web crawler in its indexing engine.
* '''[[HTTrack]]''' uses a Web crawler to create a mirror of a web site for off-line viewing. It is written in [[C (programming language)|C]] and released under the [[GNU General Public License|GPL]].
* '''[[ICDL crawling|ICDL Crawler]]''' is a [[cross-platform]] web crawler written in [[C++]] and intended to crawl Web sites based on [[Website Parse Template|Web-site Parse Template]]s using computer's free [[CPU]] resources only.
* '''[[Nutch]]''' is a crawler written in Java and released under an [[Apache License]]. It can be used in conjunction with the [[Lucene]] text-indexing package.
* '''[[Pavuk]]''' is a command-line Web mirror tool with optional X11 GUI crawler and released under the [[GNU General Public License|GPL]]. It has bunch of advanced features compared to wget and httrack, e.g., regular expression based filtering and file creation rules.
* '''[[YaCy]]''', a free distributed search engine, built on principles of peer-to-peer networks (licensed under [[GNU General Public License|GPL]]).

==See also==

*[[Distributed web crawling]]
*[[Focused crawler]]
*[[Search engine indexing|Search Engine Indexing]] - the step after crawling
*[[Spambot]]
*[[Spider trap]]
*[[Spidering Hacks]] - an O'Reilly book focused on spider-like programming
*[[Web archiving]]
*[[Website Parse Template]]

==References==
{{reflist}}
{{Cleanup|date=July 2007}}
* {{cite journal|author = Abiteboul, S., Preda, M., and Cobena, G. | title = [http://www2003.org/cdrom/papers/refereed/p007/p7-abiteboul.html Adaptive on-line page importance computation] | journal = In Proceedings of the twelfth international conference on World Wide Web | pages = 280–290 | location = Budapest, Hungary | year = 2003 | publisher = ACM Press | doi = 10.1145/775152.775192 }}
*Boldi, P., Codenotti, B., Santini, M., and Vigna, S. (2004a). [http://vigna.dsi.unimi.it/ftp/papers/UbiCrawler.pdf UbiCrawler: a scalable fully distributed Web crawler]. Software, Practice and Experience, 34(8):711–726.
*Boldi, P., Santini, M., and Vigna, S. (2004b). [http://vigna.dsi.unimi.it/ftp/papers/ParadoxicalPageRank.pdf Do your worst to make the best: Paradoxical effects in pagerank incremental computations]. In Proceedings of the third Workshop on Web Graphs (WAW), volume 3243 of Lecture Notes in Computer Science, pages 168-180, Rome, Italy. Springer.
*Burner, M. (1997). [http://www.webtechniques.com/archives/1997/05/burner/ Crawling towards eternity – building an archive of the World Wide Web]. Web Techniques, 2(5).
*Castillo, C. (2004). [http://chato.cl/research/crawling_thesis Effective Web Crawling]. PhD thesis, University of Chile.
* {{cite journal|author = Cho, J., Garcia-Molina, H., and Page, L. | title = [http://dbpubs.stanford.edu/pub/1998-51 Efficient crawling through URL ordering] | journal = In Proceedings of the seventh conference on World Wide Web | location = Brisbane, Australia | year = 1998}}
*Cho, J. and Garcia-Molina, H. (2000). [http://www.cs.brown.edu/courses/cs227/2002/cache/Cho.pdf Synchronizing a database to improve freshness]. In Proceedings of ACM International Conference on Management of Data (SIGMOD), pages 117-128, Dallas, Texas, USA.
*Cho, J. and Garcia-Molina, H. (2002). [http://portal.acm.org/citation.cfm?id=511464 Parallel crawlers]. In Proceedings of the eleventh international conference on World Wide Web, pages 124–135, Honolulu, Hawaii, USA. ACM Press.
*Cho, J. and Garcia-Molina, H. (2003). [http://portal.acm.org/citation.cfm?doid=857166.857170 Estimating frequency of change]. ACM Transactions on Internet Technology, 3(3).
* {{cite journal|author = Cothey, V. | title = [http://dx.doi.org/10.1002/asi.20078 Web-crawling reliability] |     doi = 10.1002/asi.20078 Web-crawling reliability] | journal = Journal of the American Society for Information Science and Technology | year = 2004 | volume = 55 | issue = 14}}
*Edward G. Coffman, Z. Liu, R. W. (1998). Optimal robot scheduling for Web search engines. Journal of Scheduling, 1(1):15–29.
*Heydon, A. and Najork, M. (1999). [http://www.cindoc.csic.es/cybermetrics/pdf/68.pdf Mercator: A scalable, extensible Web crawler]. World Wide Web, 2(4):219–229.
*Lawrence, S. and Giles, C. L. (2000). [http://www.nature.com/doifinder/10.1038/21987 Accessibility of information on the web]. Intelligence, 11(1), 32–39.
*Miller, R. and Bharat, K. (1998). [http://www.cs.cmu.edu/~rcm/papers/www7/www7.html  Sphinx: A framework for creating personal, site-specific web crawlers]. In Proceedings of the seventh conference on World Wide Web, Brisbane, Australia. Elsevier Science.
* {{cite journal|author = Nelson, M. L. , Van de Sompel, H. , Liu, X., Harrison, T. L. and McFarland, N. | title = [http://arxiv.org/abs/cs/0503069  mod_oai: An Apache module for metadata harvesting] | journal = In Proceedings of the 9th European Conference on Research and Advanced Technology for Digital Libraries (ECDL 2005) | pages = 509 | year = 2005}}
* {{cite conference | author = Pant, G., Srinivasan, P., Menczer, F. | year = 2004 | title = Crawling the Web | booktitle = Web Dynamics: Adapting to Change in Content, Size, Topology and Use, edited by M. Levene and A. Poulovassilis | pages = 153-178 | url = http://dollar.biz.uiowa.edu/~pant/Papers/crawling.pdf|format=PDF}}
*da Silva, A. S., Veloso, E. A., Golgher, P. B., Ribeiro-Neto, B. A., Laender, A. H. F., and Ziviani, N. (1999). [http://www.dcc.fua.br/~alti/pubs/spire99_cow.ps.gz Cobweb – a crawler for the Brazilian web]. In Proceedings of String Processing and Information Retrieval (SPIRE), pages 184–191, Cancun, Mexico. IEEE CS Press.
* Yibei Ling and Jie Mi, An optimal trade-off between content freshness and refresh cost, Journal of applied probability, 2004, vol. 41, no3, pp. 721-734.

[[Category:Search engine software]]
[[Category:Web crawlers]]
[[Category:Internet search algorithms]]

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