search engine Wiki

[edit] History of popular Web search engines Timeline Note: "Launch" refers only to web availability of original crawl-based web search engine results. Year Engine Event 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 Lexxe.com Founded 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 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. 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 engines 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. Soon after, many search engines appeared and vied for popularity. These included Excite, Infoseek, Inktomi, Northern Light, and AltaVista. Yahoo! was among the most popular ways for people to find web pages of interest, but its search function operated on its web directory, rather than full-text copies of web pages. Information seekers could also browse the directory instead of doing a keyword-based search. 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. Many search engine companies were caught up in the dot-com bubble, a speculation-driven market boom that peaked in 1999 and ended in 2001. Around 2001, the Google search engine rose to prominence.

[citation needed] The company achieved better results for many searches with an innovation called PageRank. This iterative algorithm ranks web pages based on the number and PageRank of other web sites and pages that link there, on the premise that good or desirable pages are linked to more than others. Google also maintained a minimalist interface to its search engine. In contrast, many of its competitors embedded a search engine in a web portal. By 2001, Yahoo was providing search services based on Inktomi's search engine. Yahoo! acquired Inktomi in 2002, and Overture (which owned AlltheWeb and AltaVista) in 2003. Yahoo! switched to using Google's search engine until 2004, when it launched its own search engine based on the combined technologies of its acquisitions. Microsoft first launched MSN Search (since re-branded Live Search) in the fall of 1998 using search results from Inktomi. In early 1999 the site began to display listings from Looksmart blended with results from Inktomi except for a short time in 1999 when results from AltaVista were used instead. In 2004, Microsoft began a transition to its own search technology, powered by its own web crawler (called msnbot). As of 2007, Google is the most popular Web search engine worldwide.

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[3] A number of country-specific search engine companies have become prominent; for example Baidu is the most popular search engine in the People's Republic of China.

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[edit] Current market share For a more complete list broken down by type of search, please see List of search engines. Most popular search engines worldwide, Dec. 2007

[5] Company Millions of searches Relative market share Google 28,454 46.47% Yahoo! 10,505 17.16% Baidu 8,428 13.76% Microsoft 7,880 12.87% NHN 2,882 4.71% eBay 2,428 3.9% Time Warner (includes AOL) 1,062 1.6% Ask.com and related 728 1.1% Yandex 566 0.9% Alibaba.com 531 0.8% Total 61,221 100.0%

[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 Web crawling Indexing 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.

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[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 Search engine test 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). ^ "Internet History - Search Engines" (from Search Engine Watch), Universiteit Leiden, Netherlands, September 2001, web: LeidenU-Archie. ^ Nielsen NetRatings: August 2007 Search Share Puts Google On Top, Microsoft Holding Gains, SearchEngineLand, September 21, 2007 ^ comScore: August 2007 Google Top Worldwide Search Engine; Baidu Beats Microsoft ^ MSN Money - BIDU. MSN Money. Retrieved on 2006-05-11. ^ http://www.comscore.com/press/release.asp?press=2018

[edit] Further reading For a more detailed history of early search engines, see "Search Engine Birthdays" (from Search Engine Watch), Chris Sherman, September 2003, web: SearchEngineWatch article Steve Lawrence; C. Lee Giles (1999). "Accessibility of information on the web". Nature 400: 107. doi:10.1038/21987.  Javed Mostafa (February 2005). "Seeking Better Web Searches". Scientific American Magazine. 

[edit] External links 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. Search Engines: costs vs. benefits History of Search Engines Retrieved from "http://en.wikipedia.org/wiki/Web_search_engine" Categories: All articles with unsourced statements | Articles with unsourced statements since February 2008 | Articles needing additional references from November 2007 | Articles with unsourced statements since February 2007 | Articles to harmonize | Information retrieval | Internet search engines | Internet terminology | Search engine optimization