A search engine or search service is a document 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. Search engines use regularly updated indexes to operate quickly and efficiently.
History
The very first tool used for searching on the Internet was Archie. [1] (The name stands for "archive" without the "v", not the character from the 'Archie' comic book series). 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, but 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.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 was 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. 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 Dr. 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.
How search engines work
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 comes to the search engine and makes a query, typically by giving key words, the engine looks up the 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 terms AND, OR and NOT to further specify the search query. An advanced feature is 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 everytime someone clicks on one of these ads.
The vast majority of search engines are run by private companies using proprietary algorithms and closed databases, the most popular currently being Google, MSN Search, and Yahoo! Search. However, Open source search engine technology does exist, such as ht://Dig, Nutch, Senas, Egothor, OpenFTS, DataparkSearch and many others.
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 100TB or so for indexes, giving a total hardware cost of under $200k: 400 500GB disk drives on 100 cheap PCs.
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.
History
The very first tool used for searching on the Internet was Archie. [1] (The name stands for "archive" without the "v", not the character from the 'Archie' comic book series). 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, but 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.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 was 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. 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 Dr. 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.
How search engines work
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 comes to the search engine and makes a query, typically by giving key words, the engine looks up the 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 terms AND, OR and NOT to further specify the search query. An advanced feature is 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 everytime someone clicks on one of these ads.
The vast majority of search engines are run by private companies using proprietary algorithms and closed databases, the most popular currently being Google, MSN Search, and Yahoo! Search. However, Open source search engine technology does exist, such as ht://Dig, Nutch, Senas, Egothor, OpenFTS, DataparkSearch and many others.
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 100TB or so for indexes, giving a total hardware cost of under $200k: 400 500GB disk drives on 100 cheap PCs.
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.
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