Search engine

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. 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 2000 Teoma Founded 2003 Objects Search Launch 2004 yahoo! Search Final launch (first original results) MSN Search Beta launch 2005 MSN Search Final launch FinQoo Meta Search Final launch rowspan="3" 2006 Quaero Founded 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. El primero "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 web page, which became the standard for all major search engines since. It was also the first one to be widely known by the public. También en 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. De alguna manera, they competed with popular directories such as Yahoo!. Más tarde, 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, recording 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. Before the advent of the Web, there were search engines for other protocols or uses, such as the Archie search engine for anonymous FTP sites and the Veronica search engine for the Gopher protocol. More recently search engines are also coming online which utilise XML or RSS. This allows the search engine to efficiently index data about websites without requiring a complicated crawler. The websites simply provide an xml feed which the search engine indexes. XML feeds are increasingly provided automatically by weblogs or blogs. Examples of this type of search engine are feedster, with niche examples such as LjFind Search providing search services for Livejournal blogs. Google Around 2001, the Google search engine rose to prominence. Its success was based in part on the concept of link popularity and PageRank. How many other web sites and web pages 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 web sites 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 lab. Google is currently the most popular search engine. Yahoo! Search 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 web site Sin embargo, en 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, owned by Microsoft, which previously relied on others for its search engine listings. En 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. This meant the market was now dominated by Google, yahoo, and Microsoft. The other large (self described) search engines tend to be "portals" that merely show the results another company's search engine (like MSN Search used to do). The other "verdadero" search engines (those that provide their own results), like Gigablast, have vastly less market presence than the big three. Sin embargo, since site usage is proprietary information, it's often difficult to determine which sites are most popular. Challenges faced by search engines The web is growing much faster than any present-technology search engine can possibly index (see distributed web crawling). Many web pages are updated frequently, which forces the search engine to revisit them periodically. The queries one can make are currently limited to searching for key words, which may result in many false positives. Dynamically generated sites may be slow or difficult to index, or may result in excessive results from a single site. Many dynamically generated sites are not indexable by search engines; this phenomenon is known as the invisible web. Some search engines do not order the results by relevance, but rather according to how much money the sites have paid them. Some sites use tricks to manipulate the search engine to display them as the first result returned for some keywords. This can lead to some search results being polluted, with more relevant links being pushed down in the result list. How search engines work A search engine operates, in the following order Web crawling Deep Crawling Depth-first search (DFS) Fresh Crawling Breadth-first search (BFS) 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 (por ejemplo, words are extracted from the titles, headings, or special fields called meta tags). Data about web pages is 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 some store every word of every page it finds, such as AltaVista. 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 web page. 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 you to define the distance between keywords. The usefulness of a search engine depends on the relevance of the results it gives back. While there may be millions of Web pages 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. 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! Buscar. Sin embargo, Open source search engine technology does exist, such as ht://Dig, Nutch, Senas, Egothor, OpenFTS, DataparkSearch and many others. Storage costs and crawling time A modern search engine can operate with stunningly modest storage requirements. Assuming a 500GB drive costs $100 USD (2006 prices), and that an average web page compresses to less than 10K, 10B pages would compress to 100TB, thus using 200 hard drives and cost around $20K. If we kept 4 drives per server, we would need no more than 50 servers to store these 10B pages, which at $250/computer would cost under $12.5K. Thus it is possible to have 10B pages held on a server farm with no more than $35K of hardware. Most search engines, in order to serve millions of search queries, serve ads and process the crawl have considerably more resources than this, and allow for significant redundancy to handle disruptions in service. Crawling 10B pages with 100 machines crawling at 100 pages/second would take 1M seconds, o 11.6 Días. Most search engines crawl a small fraction of the web (10-20% Páginas) at around this frequency or better, but also crawl dynamic web sites (p. ej.. news sites and blogs) at a much higher frequency.. Geospatially enabled search engines MetaCarta Search technology integrated into USEPA's Window to My Environment application A recent enhancement to search engine technology is the addition of geocoding to the processing of the ingested documents. Geocoding 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 geocoding 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. One company that has developed this type of technology is MetaCarta, which makes its search technology also available as an XML Web Service to allow deep integration into existing applications. MetaCarta also provides an extension for desktop GIS software such as ESRI's ArcGIS, to allow analysts to interactively query the search engine and retrieve documents in an advanced geospatial and analytical context. See also Data mining History of the Internet Inverted index List of search engines Local search Metasearch engine Search artist Search engine marketing Search engine optimization Spamdexing Web indexing Books John Battelle, The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture, ISBN 1591840880, 2005 Links The Web Index - contains a large list of search engines ar:محرك بحث cs:Internetový vyhledávač da:Søgemaskine de:Suchmaschine eo:Serĉilo es:Buscador fa:موتور جستجو fi:Hakukone fr:Moteur de recherche he:מנוע חיפוש hu:Keresőmotor ia:Mechanismo de recerca id:Mesin pencari ko:검색 엔진 lv:Meklis ms:Enjin gelintar nl:Zoekmachine pt:Motor de busca ru:Поисковая система simple:Search engine sv:Sökmaskiner ta:தேடல் பொறி el:เครื่องมือค้นหาบนอินเทอร์เน็ต uk:Пошукова машина vi:Máy truy tìm dliu zh:搜索引擎 Esta página utiliza contenido con licencia Creative Commons de Wikipedia (ver autores).

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