Web Search Engine in Wiki

Web search engine

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Google search is the world's most popular search engine.

Web search engines provide an interface to search for information on the World Wide Web. Information may consist of web pages, images and other types of files.

Some search engines also mine data available in newsgroups, databases, or open directories. Unlike Web directories, which are maintained by human editors, search engines operate algorithmically or are a mixture of algorithmic and human input.

Contents

• 1 History of popular Web search engines
  • 1.1 Google
  • 1.2 Yahoo! Search
  • 1.3 Microsoft
  • 1.4 Baidu
• 2 Challenges faced by Web search engines
• 3 How Web search engines work
• 4 Storage costs and crawling time
• 5 Geospatially-enabled Web search engines
• 6 Social Web search
• 7 See also
• 8 Notes
• 9 References
• 10 External links

[edit] History of popular Web search engines

The very first tool used for searching on the Internet was Archie.^[1] The name stands for "archive" without the "vee". It was created in 1990 by Alan Emtage, a student at McGill University in Montreal. The program downloaded the directory listings of all the files located on public anonymous FTP (File Transfer Protocol) sites, creating a searchable database of file names; however, Archie did not index the contents of these files.

The rise of Gopher (created in 1991 by Mark McCahill at the University of Minnesota) led to two new search programs, Veronica and Jughead. Like Archie, they searched the file names and titles stored in Gopher index systems. Veronica (Very Easy Rodent-Oriented Net-wide Index to Computerized Archives) provided a keyword search of most Gopher menu titles in the entire Gopher listings. Jughead (Jonzy's Universal Gopher Hierarchy Excavation And Display) was a tool for obtaining menu information from specific Gopher servers. While the name of the search engine "Archie" was not a reference to the Archie comic book series, "Veronica" and "Jughead" are characters in the series, thus referencing their predecessor.

Timeline Year Engine Event

Note: "Launch" refers only to web availability of original crawl-based web search engine results.
1993	Aliweb	Launch
1994	WebCrawler	Launch
JumpStation	Launch
Infoseek	Launch
	Lycos	Launch
1995	AltaVista	Launch (part of DEC)
Excite	Launch
1996	Dogpile	Launch
Inktomi	Founded
HotBot	Founded
	Ask Jeeves	Founded
1997	Northern Light	Launch
1998	Google	Launch
1999	AlltheWeb	Launch
Naver	Launch
Teoma	Founded
Vivisimo	Founded
2000	Baidu	Founded
2003	Info.com	Launch
2004	Yahoo! Search	Final launch
	A9.com	Launch
2005	MSN Search	Final launch
Ask.com	Launch
AskMeNow	Launch
2006	wikiseek	Founded
Quaero	Founded
Ask.com	Launch
Live Search	Launch
ChaCha	Beta Launch
Quintura	Beta Launch
Guruji.com	Beta Launch
2007	wikiseek	Launched
AskWiki	Launched

The first Web search engine was Wandex, a now-defunct index collected by the World Wide Web Wanderer, a web crawler developed by Matthew Gray at MIT in 1993. Another very early search engine, Aliweb, also appeared in 1993, and still runs today. JumpStation (released in early 1994) used a crawler to find web pages for searching, but search was limited to the title of web pages only. One of the first "full text" crawler-based search engine was WebCrawler, which came out in 1994. Unlike its predecessors, it let users search for any word in any webpage, which became the standard for all major search engines since. It was also the first one to be widely known by the public. Also in 1994 Lycos (which started at Carnegie Mellon University) was launched, and became a major commercial endeavor. For a more detailed history of early search engines, see ^[2].

Soon after, many search engines appeared and vied for popularity. These included Excite, Infoseek, Inktomi, Northern Light, and AltaVista. In some ways, they competed with popular directories such as Yahoo!. Later, the directories integrated or added on search engine technology for greater functionality.

Search engines were also known as some of the brightest stars in the Internet investing frenzy that occurred in the late 1990s. Several companies entered the market spectacularly, receiving record gains during their initial public offerings. Some have taken down their public search engine, and are marketing enterprise-only editions, such as Northern Light.

[edit] Google

Around 2001, the Google search engine rose to prominence. Its success was based in part on the concept of link popularity and PageRank. The number of other websites and webpages that link to a given page is taken into consideration with PageRank, on the premise that good or desirable pages are linked to more than others. The PageRank of linking pages and the number of links on these pages contribute to the PageRank of the linked page. This makes it possible for Google to order its results by how many websites link to each found page. Google's minimalist user interface is very popular with users, and has since spawned a number of imitators.

Google and most other web engines utilize not only PageRank but more than 150 criteria to determine relevancy.^[3] The algorithm "remembers" where it has been and indexes the number of cross-links and relates these into groupings. PageRank is based on citation analysis that was developed in the 1950s by Eugene Garfield at the University of Pennsylvania. Google's founders cite Garfield's work in their original paper. In this way virtual communities of webpages are found. Teoma's search technology uses a communities approach in its ranking algorithm. NEC Research Institute has worked on similar technology. Web link analysis was first developed by Jon Kleinberg and his team while working on the CLEVER project at IBM's Almaden Research Center. Google is currently the most popular Web search engine.^[4][5]

[edit] Yahoo! Search

The two founders of Yahoo!, David Filo and Jerry Yang, Ph.D. candidates in Electrical Engineering at Stanford University, started their guide in a campus trailer in February 1994 as a way to keep track of their personal interests on the Internet. Before long they were spending more time on their home-brewed lists of favourite links than on their doctoral dissertations. Eventually, Jerry and David's lists became too long and unwieldy, and they broke them out into categories. When the categories became too full, they developed subcategories ... and the core concept behind Yahoo! was born. In 2002, Yahoo! acquired Inktomi and in 2003, Yahoo! acquired Overture, which owned AlltheWeb and AltaVista. Despite owning its own search engine, Yahoo! initially kept using Google to provide its users with search results on its main website Yahoo.com. However, in 2004, Yahoo! launched its own search engine based on the combined technologies of its acquisitions and providing a service that gave pre-eminence to the Web search engine over the directory.

[edit] Microsoft

The most recent major search engine is MSN Search (evolved into Live Search), owned by Microsoft, which previously relied on others for its search engine listings. In 2004, it debuted a beta version of its own results, powered by its own web crawler (called msnbot). In early 2005 , it started showing its own results live, and ceased using results from Inktomi, now owned by Yahoo!. In 2006, Microsoft migrated to a new search platform - Live Search, retiring the "MSN Search" name in the process.

[edit] Baidu

Baidu was launched in 2000 and is the leading Chinese search engine, providing an index of over 740 million web pages, 80 million images, and 10 million multimedia files.^[6] Its interface is very similar to Google's.

[edit] Challenges faced by Web search engines

This section does not cite any references or sources. Please improve this section by adding citations to reliable sources. Unverifiable material may be challenged and removed. (November 2007)

• The Web is growing much faster than any present-technology search engine can possibly index (see Distributed web crawling).
• A web page must be reindexed each time it is changed.
• The Web search queries one can make are currently limited to searching for keywords, which may result in many Type I and type II error positives, especially using the default whole-page search. Better results might be achieved by using a proximity search option with a search-bracket to limit matches within a paragraph or phrase, rather than matching random words scattered across large pages. Another alternative is using human operators to do the researching for 'organic' search engine users.
• Dynamically generated sites may be slow or difficult to index, or may result in excessive results, perhaps generating 500 times more web pages than average. Example: for a dynamic webpage which changes content based on entries inserted from a database, a search engine might be requested to index 50,000 static web pages for 50,000 different parameter values passed to that dynamic webpage. The indexing is numerous in the dynamic web pages, they can also be shown by logical thinking: if one parameter-value generates 1 indexed webpage, 10 generate 10, and 1,000 parameter-values generate 1,000 web pages, etc. Also, some dictionary-page websites are indexed using dynamic pages: for example, search for page-counts of URLs containing variations of "dictionary.*" and observe the page-totals reported by the search engines, perhaps in excess of 50,000 pages.
• Many dynamically generated websites are not indexable by search engines; this phenomenon is known as the invisible web. Some search engines specialize in crawling dynamic content on the invisible web that is password protected or requires forms to be filled out.
• Relevancy: sometimes an engine can't find what the person is looking for. It may give a list of unwanted, irrelevant sites, electronic spam, or pop-ups.
• Some search engines do not rank results by relevance, but by the amount of money paid by websites to appear in the results.
• Many websites use tricks to ensure they are listed higher in search results, for numerous keywords. This can lead to search engine results being polluted with linkspam or bait-and-switch pages which contain little or no information about the matching phrases. Genuinely relevant web pages are pushed further down results lists. For example, many spammers create websites containing random sequences of high-traffic keywords, often with misspellings in order to attract a higher ranking on a search engine.
• Secure content hosted on HTTPS URLs pose a challenge for crawlers which either can't browse the content for technical reasons or won't index it for privacy reasons.

[edit] How Web search engines work

This section does not cite any references or sources. Please improve this section by adding citations to reliable sources. Unverifiable material may be challenged and removed. (November 2007)

A search engine operates, in the following order

1. Web crawling
2. Indexing
3. Searching

Web search engines work by storing information about a large number of web pages, which they retrieve from the WWW itself. These pages are retrieved by a Web crawler (sometimes also known as a spider) — an automated Web browser which follows every link it sees. Exclusions can be made by the use of robots.txt. The contents of each page are then analyzed to determine how it should be indexed (for example, words are extracted from the titles, headings, or special fields called meta tags). Data about web pages are stored in an index database for use in later queries. Some search engines, such as Google, store all or part of the source page (referred to as a cache) as well as information about the web pages, whereas others, such as AltaVista, store every word of every page they find. This cached page always holds the actual search text since it is the one that was actually indexed, so it can be very useful when the content of the current page has been updated and the search terms are no longer in it. This problem might be considered to be a mild form of linkrot, and Google's handling of it increases usability by satisfying user expectations that the search terms will be on the returned webpage. This satisfies the principle of least astonishment since the user normally expects the search terms to be on the returned pages. Increased search relevance makes these cached pages very useful, even beyond the fact that they may contain data that may no longer be available elsewhere.

When a user enters a query into a search engine (typically by using key words), the engine examines its index and provides a listing of best-matching web pages according to its criteria, usually with a short summary containing the document's title and sometimes parts of the text. Most search engines support the use of the boolean operators AND, OR and NOT to further specify the search query. Some search engines provide an advanced feature called proximity search which allows users to define the distance between keywords.

The usefulness of a search engine depends on the relevance of the result set it gives back. While there may be millions of webpages that include a particular word or phrase, some pages may be more relevant, popular, or authoritative than others. Most search engines employ methods to rank the results to provide the "best" results first. How a search engine decides which pages are the best matches, and what order the results should be shown in, varies widely from one engine to another. The methods also change over time as Internet usage changes and new techniques evolve.

Most Web search engines are commercial ventures supported by advertising revenue and, as a result, some employ the controversial practice of allowing advertisers to pay money to have their listings ranked higher in search results. Those search engines which do not accept money for their search engine results make money by running search related ads alongside the regular search engine results. The search engines make money every time someone clicks on one of these ads.

The vast majority of search engines are run by private companies using proprietary algorithms and closed databases, though some are open source.^{[citation needed]}

[edit] Storage costs and crawling time

This article or section may contain original research or unverified claims. Please improve the article by adding references. See the talk page for details. (September 2007)

Storage costs are not the limiting resource in search engine implementation. Simply storing 10 billion pages of 10 kilobytes each (compressed) requires 100 TB and another 100 TB or so for indexes, giving a total hardware cost of under $200k: 100 cheap PCs each with four 500 GB disk drives.^{[citation needed]}

However, a public search engine requires considerably more resources than this to calculate query results and to provide high availability. Also, the costs of operating a large server farm are not trivial.^{[citation needed]}

Crawling 10B pages with 100 machines crawling at 100 pages/second would take 1M seconds, or 11.6 days on a very high capacity Internet connection. Most search engines crawl a small fraction of the Web (10-20% pages) at around this frequency or better, but also crawl dynamic websites (e.g. news sites and blogs) at a much higher frequency.^{[citation needed]}

[edit] Geospatially-enabled Web search engines

The following text needs to be harmonized with text in the article Local search (Internet). (See e.g. Wikipedia:Summary style.)

Main article: Local search (Internet)

A recent enhancement to search engine technology is the addition of geocoding and geoparsing to the processing of the ingested documents being indexed, to enable searching within a specified locality (or region). Geoparsing attempts to match any found references to locations and places to a geospatial frame of reference, such as a street address, gazetteer locations, or to an area (such as a polygonal boundary for a municipality).^{[citation needed]} Through this geoparsing process, latitudes and longitudes are assigned to the found places, and these latitudes and longitudes are indexed for later spatial query and retrieval. This can enhance the search process tremendously by allowing a user to search for documents within a given map extent, or conversely, plot the location of documents matching a given keyword to analyze incidence and clustering, or any combination of the two. See the list of search engines for examples of companies which offer this feature.

[edit] Social Web search

Further information: Social search

Social search engines are a type of vertical search engine found on many websites^{[citation needed]}.

[edit] See also

• List of search engines
• Federated search
• Inverted index
• Metasearch engine
• Organic search
• Page hijacking
• Search engine marketing
• Search oriented architecture
• Index (search engine)
• Spamdexing
• Vertical search
• Video search engine
• Web indexing
• Web search query

[edit] Notes

The footnotes below are given in support of the statements above. Because some facts are proprietary secrets held by private companies and therefore not documented in journals, such facts are reasoned from facts that are public.

• GBMW: Reports of 30-day punishment, re: Car maker BMW had its German website bmw.de delisted from Google, such as: Slashdot-BMW (05-Feb-2006).
• INSIZ: Maximum size of webpages indexed by MSN/Google/Yahoo! ("100-kb limit"): Max Page-size (28-Apr-2006).

1. ^ "Internet History - Search Engines" (from Search Engine Watch), Universiteit Leiden, Netherlands, September 2001, web: LeidenU-Archie.
2. ^ "Search Engine Birthdays" (from Search Engine Watch), Chris Sherman, September 2003, web: SearchEngineWatch article.
3. ^ Sergey Brin and Lawrence Page. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Stanford University. 1998.
4. ^ Nielsen NetRatings: August 2007 Search Share Puts Google On Top, Microsoft Holding Gains, SearchEngineLand, September 21, 2007
5. ^ comScore: August 2007 Google Top Worldwide Search Engine; Baidu Beats Microsoft
6. ^ MSN Money - BIDU. MSN Money. Retrieved on 2006-05-11.

[edit] References

• "Seeking Better Web Searches," Scientific American magazine (February 2005 Issue).

[edit] External links

• Scientific American Magazine (February 2005 Issue) "Seeking Better Web Searches"
• Search engine aids rights workers
• How Google Finds Your Needle in the Web's Haystack The mathematics of calculating website importance.
• Enterprise Search Analysis Network Computing's Review of enterprise search and eight enterprise search vendors.

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Categories: Articles needing additional references from November 2007 | All articles with unsourced statements | Articles with unsourced statements since February 2007 | Articles that may contain original research since September 2007 | Articles to harmonize | Information retrieval | Internet search engines | Internet terminology | Search engine optimization

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