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A search engine is an information retrieval system designed to help find information stored on a computer system, such as on the World Wide Web, inside a corporate or proprietary network, or in a personal computer. The search engine allows one to ask for content meeting specific criteria (typically those containing a given word or phrase) and retrieves a list of items that match those criteria. This list is often sorted with respect to some measure of relevance of the results. Search engines use regularly updated indexes to operate quickly and efficiently.
Without further qualification, search engine usually refers to a Web search engine, which searches for information on the public Web. Other kinds of search engine are enterprise search engines, which search on intranets, personal search engines, and mobile search engines. Different selection and relevance criteria may apply in different environments, or for different uses.
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 |
History
The very first tool used for searching on the Internet was Archie.[1] The name stands for "archive" without the "v". 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 | ||
|---|---|---|
| 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 | 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 | |
| 2007 | wikiseek | Launched |
| Tokenizer | 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.
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.
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.[2] 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. Nippon Electric Corporation|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 search engine.
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.
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.
Baidu
Due to the difference between hanzi and the Roman alphabet, the Chinese search market did not boom until the introduction of Baidu in 2000.[citation needed]
Top Providers
| Market Share worlwide as of June, 2007 [3] |
|---|
| Google (all) - above 74% |
| Google.com - 51.53% |
| Yahoo! Web Sites - 12.16% |
| Google UK - 9.51% |
| MSN - 4.34% |
| Google AdSense for Content - 3.51% |
| Google Canada - 3.17% |
| AOL - 2.06% |
| Top U.S. Search Providers by Searches, May 2007: | ||
| Provider | Searches (000) | Share of Total Searches (%) |
|---|---|---|
| 4,033,277 | 56.3 | |
| Yahoo | 1,540,949 | 21.5 |
| MSN/Windows Live | 605,400 | 8.4 |
| AOL | 381,961 | 5.3 |
| Ask.com | 142,418 | 2.0 |
| My Web Search | 61,784 | 0.9 |
| Comcast | 34,908 | 0.5 |
| EarthLink | 33,461 | 0.5 |
| My Way | 30,122 | 0.4 |
| Dogpile.com | 26,295 | 0.4 |
| Other | 275,365 | 3.8 |
| All search | 7,165,940 | 100.0 |
| Source: Nielsen//NetRatings, 2007 |
Challenges faced by search engines
- The Web is growing much faster than any present-technology search engine can possibly index see distributed web crawling. In 2006, some users found major search-engines became slower to index new webpages. Time to index in MSN Search, slowing down in Dec-2005 & Jan-2006:(18-Jan-2006).
- Many webpages are updated frequently, which forces the search engine to revisit them periodically.
- The Web search queries one can make are currently limited to searching for key words, which may result in many Type I and type II errors positives, especially using the default whole-page search. Better results might be achieved by using a Proximity search (text) 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 the user with organic search engines.
- Dynamically generated sites may be slow or difficult to index, or may result in excessive results, perhaps generating 500 times more webpages 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 webpages for 50,000 different parameter values passed to that dynamic webpage. The indexing is numerous in the dynamic webpages, 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 webpages, 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. There are list of search engines that specialize in crawling the invisible web by crawling sites that have dynamic content, require forms to be filled out, or are password protected.
- Relevancy: sometimes the engine can't get what the person is looking for.
- Some search-engines do not rank results by relevance, but by the amount of money the matching websites pay.
- In 2006, hundreds of generated websites used tricks to manipulate a search-engine to display them in the higher results for numerous keywords. This can lead to some search engine results being polluted with linkspam or bait-and-switch pages which contain little or no information about the matching phrases. The more relevant webpages are pushed further down in the results list, perhaps by 500 entries or more.
LSPAM: The number of spam-links that slip past search-engine restrictions
is a company-proprietary secret. Hundreds of spam-links can be verified
by searching 24 variations of spelling "Da Vinci"/"Davinci" such as
"Devinchi" or "Davinche" and counting the matching links which contain
no relevant details, such as: "devinchi com. ketogenic
diet recipe kona coffee label" -- searches can be repeated
with numerous other rare words. (Such searches will yield definitive
results for years and will, of course, be the most current research on the
subject. See also: linkspam explaining proliferation of spam-pages.
- Secure pages 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.
How search engines work
A search engine operates, in the following order
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]
Storage costs and crawling time
 | This article or section may contain original research or unverified claims. Please help Wikipedia by adding references. See the talk page for details. |
Storage costs are not the limiting resource in search engine implementation. Simply storing 10 billion pages of 10 kbytes each (compressed) requires 100TB and another 100 TB or so for indexes, giving a total hardware cost of under $200k: 100 cheap PCs each with four 500GB 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]
Geospatially enabled search engines
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.
Vertical Search Engines
Vertical search engines or specialized search engines are search engines which specialize in specific content categories or that search within a specific medium. Popular search engines, such as Google or Yahoo!, are very effective when the user searches for web sites, web pages, or general information. Vertical search engines enable the user to find specific types of listings, thus making the search more customized to the user's needs.
Category-focused vertical search engines
This type of search engine searches for sites, pages, and other online content relevant to a specific area or category. Vertical search engines falling into this category include shopping search engines (such as Froogle or NexTag), government search engines (such as "Google US government search" and searchgov.com), legal search engines (such as law.com and lawcrawler), travel search engines (such as Travelocity and Expedia), financial search engines (such as Business.com and Hoovers), business search engines (such as knuru) and many others.
Media-focused search engines
This type of search engine focuses on searching within specific online media. This group includes the following subcategories, although this list is not exhaustive:
- Forum and discussion group search engines which scan discussion boards, forums, groups, answer pages and other "many to many" online media. Examples of search engines of this kind include Omgili and board-tracker.
- News group search engines which scan news groups worldwide. This category includes bincrawler, Google groups, knuru and many others.
- Blog search engines focus on searching web logs or "the blogosphere." Some of the members of this group are: Google Blog Search, Technorati, knuru, and Blog-search-engine.
- Mailing list search engines search in mailing lists. These include "list of lists" and E-Zine List
- Chat search engines search on chat rooms. Examples of this subcategory are Chatsearch and Search IRC.
Social search
- Further information: Social search
Social search engines are a type of vertical search engine found on many websites[citation needed].
See also
- List of search engines
- Automatic summarization
- Bibliographic database
- Desktop search
- Federated search
- Image search
- Inverted index
- Instant indexing
- Local search
- Medical literature retrieval
- Metasearch engine
- Organic search
- Page hijacking
- Search engine marketing
- Search engine optimization
- Search oriented architecture
- Semantic search
- Index (search engine)
- Spamdexing
- Sponsored link
- Text mining
- Vertical search
- Web indexing
- Web search query
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.
- ^ Sergey Brin and Lawrence Page. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Stanford University. 1998.
- ^ http://marketshare.hitslink.com/report.aspx?qprid=1
References
- "Seeking Better Web Searches," Scientific American magazine (February 2005 Issue).
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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