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Search engine
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.
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 filenames;
however, Archie could not search by file contents.
While Archie indexed computer files, Gopher indexed plain text documents.
Gopher was created in 1991 by Mark McCahill at the University of Minnesota:
Gopher was named after the school's mascot.[1] Because these were text
files, most of the Gopher sites became websites after the creation of the
World Wide Web.
Two other programs, Veronica and Jughead, searched the files 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
various 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
Infoseek Launch
Lycos Launch
1995 AltaVista Launch (part of DEC)
Excite Launch
1996 Dogpile Launch
Inktomi 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
2005 MSN Search Final launch
Ask.com Launch
AskMeNow Launch
2006 wikiseek Founded
Quaero Founded
Ask.com Launch
Windows 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. 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)
came out, 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.
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.Sergey Brin and Lawrence Page. The
Anatomy of a Large-Scale Hypertextual Web Search Engine. Stanford
University. 1998. 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. This was barely noticed by average
users unaware of where results come from, but was a huge development for
many webmasters, who seek inclusion in the major search engines. At the same
time, Microsoft 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.
Top Providers
Market Share worlwide as of June, 2007 [2]
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 (%)
Google 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
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.
Storage costs and crawling time
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.
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.
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.
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).
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:
1. 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.
2. News group search engines which scan news groups worldwide. This category
includes bincrawler, Google groups, knuru and many others.
3. 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.
4. Mailing list search engines search in mailing lists. These include "list
of lists" and E-Zine List
5. Chat search engines search on chat rooms. Examples of this subcategory
are Chatsearch and Search IRC.
Social search
Social search engines are a type of vertical search engine found on many
websites.
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. ^ a b "Internet History - Search Engines" (from Search Engine Watch),
Universiteit Leiden, Netherlands, September 2001, web: LeidenU-Archie.
2. ^ http://marketshare.hitslink.com/report.aspx?qprid=1
References
* "Seeking Better Web Searches," Scientific American magazine (February 2005
Issue).
read the copyright
Current News: a dictionary of current search terms and popular interest topics.