search engine optimization

 search engine optimization

A search engine is a software system designed to carry out web searches. They search the World Wide Web in a systematic way for particular information specified in a textual web search query. The search results are generally presented in a line of results, often referred to as search engine results pages. When a user enters a query into a search engine, the engine scans its index of web pages to find those that are relevant to the user's query. The results are then ranked by relevancy and displayed to the user. The information may be a mix of links to web pages, images, videos, infographics, articles, research papers, and other types of files. Some search engines also mine data available in databases or open directories. Unlike web directories and social bookmarking sites, which are maintained by human editors, search engines also maintain real-time information by running an algorithm on a web crawler. Any internet-based content that cannot be indexed and searched by a web search engine falls under the category of deep web.

History

Pre-1990s 

A system for locating published information intended to overcome the ever increasing difficulty of locating information in ever-growing centralized indices of scientific work was described in 1945 by Vannevar Bush, who wrote an article in The Atlantic Monthly titled "As We May Think" in which he envisioned libraries of research with connected annotations not unlike modern hyperlinks. Link analysis would eventually become a crucial component of search engines through algorithms such as Hyper Search and PageRank.

1990s: Birth of search engines

The first internet search engines predate the debut of the Web in December 1990: WHOIS user search dates back to 1982, and the Knowbot Information Service multi-network user search was first implemented in 1989. The first well documented search engine that searched content files, namely FTP files, was Archie, which debuted on 10 September 1990.

Prior to September 1993, the World Wide Web was entirely indexed by hand. There was a list of webservers edited by Tim Berners-Lee and hosted on the CERN webserver. One snapshot of the list in 1992 remains, but as more and more web servers went online the central list could no longer keep up. On the NCSA site, new servers were announced under the title "What's New!".

The first tool used for searching content on the Internet was Archie. The name stands for "archive" without the "v". computer science student at McGill University in Montreal, Quebec, Canada. The program downloaded the directory listings of all the files located on public anonymous FTP sites, creating a searchable database of file names; however, Archie Search Engine did not index the contents of these sites since the amount of data was so limited it could be readily searched manually.

The rise of Gopher led to two new search programs, Veronica and Jughead. Like Archie, they searched the file names and titles stored in Gopher index systems. Veronica provided a keyword search of most Gopher menu titles in the entire Gopher listings. Jughead was a tool for obtaining menu information from specific Gopher servers. While the name of the search engine "Archie Search Engine" was not a reference to the Archie comic book series, "Veronica" and "Jughead" are characters in the series, thus referencing their predecessor.

In the summer of 1993, no search engine existed for the web, though numerous specialized catalogues were maintained by hand. Oscar Nierstrasz at the University of Geneva wrote a series of Perl scripts that periodically mirrored these pages and rewrote them into a standard format. This formed the basis for W3Catalog, the web's first primitive search engine, released on September 2, 1993.

In June 1993, Matthew Gray, then at MIT, produced what was probably the first web robot, the Perl-based World Wide Web Wanderer, and used it to generate an index called "Wandex". The purpose of the Wanderer was to measure the size of the World Wide Web, which it did until late 1995. The web's second search engine Aliweb appeared in November 1993. Aliweb did not use a web robot, but instead depended on being notified by website administrators of the existence at each site of an index file in a particular format.

JumpStation used a web robot to find web pages and to build its index, and used a web form as the interface to its query program. It was thus the first WWW resource-discovery tool to combine the three essential features of a web search engine as described below. Because of the limited resources available on the platform it ran on, its indexing and hence searching were limited to the titles and headings found in the web pages the crawler encountered.

One of the first "all text" crawler-based search engines was WebCrawler, which came out in 1994. Unlike its predecessors, it allowed users to search for any word in any webpage, which has become the standard for all major search engines since. It was also the search engine that was widely known by the public. Also in 1994, Lycos was launched and became a major commercial endeavor.

The first popular search engine on the Web was Yahoo! Search. The first product from Yahoo!, founded by Jerry Yang and David Filo in January 1994, was a Web directory called Yahoo! Directory. In 1995, a search function was added, allowing users to search Yahoo! Directory. It became one of the most popular ways for people to find web pages of interest, but its search function operated on its web directory, rather than its full-text copies of web pages.

Soon after, a number of search engines appeared and vied for popularity. These included Magellan, Excite, Infoseek, Inktomi, Northern Light, and AltaVista. Information seekers could also browse the directory instead of doing a keyword-based search

In 1996, Robin Li developed the RankDex site-scoring algorithm for search engines results page ranking and received a US patent for the technology. It was the first search engine that used hyperlinks to measure the quality of websites it was indexing, predating the very similar algorithm patent filed by Google two years later in 1998. Larry Page referenced Li's work in some of his U.S. patents for PageRank. Li later used his Rankdex technology for the Baidu search engine, which was founded by him in China and launched in 2000.

In 1996, Netscape was looking to give a single search engine an exclusive deal as the featured search engine on Netscape's web browser. There was so much interest that instead Netscape struck deals with five of the major search engines: for $5 million a year, each search engine would be in rotation on the Netscape search engine page. The five engines were Yahoo!, Magellan, Lycos, Infoseek, and Excite.

Google adopted the idea of selling search terms in 1998, from a small search engine company named goto.com. This move had a significant effect on the search engine business, which went from struggling to one of the most profitable businesses in the Internet.

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 March 2000.

2000s–present: Post dot-com bubble 

Around 2000, Google's search engine rose to prominence. The company achieved better results for many searches with an algorithm called PageRank, as was explained in the paper Anatomy of a Search Engine written by Sergey Brin and Larry Page, the later founders of Google.

Web search engines get their information by web crawling from site to site. The "spider" checks for the standard filename robots.txt, addressed to it. The robots.txt file contains directives for search spiders, telling it which pages to crawl and which pages not to crawl. After checking for robots.txt and either finding it or not, the spider sends certain information back to be indexed depending on many factors, such as the titles, page content, JavaScript, Cascading Style Sheets, headings, or its metadata in HTML meta tags. After a certain number of pages crawled, amount of data indexed, or time spent on the website, the spider stops crawling and moves on. "o web crawler may actually crawl the entire reachable web. Due to infinite websites, spider traps, spam, and other exigencies of the real web, crawlers instead apply a crawl policy to determine when the crawling of a site should be deemed sufficient. Some websites are crawled exhaustively, while others are crawled only partially".

Indexing means associating words and other definable tokens found on web pages to their domain names and HTML-based fields. The associations are made in a public database, made available for web search queries. A query from a user can be a single word, multiple words or a sentence. The index helps find information relating to the query as quickly as possible. The index already has the names of the sites containing the keywords, and these are instantly obtained from the index. The real processing load is in generating the web pages that are the search results list: Every page in the entire list must be weighted according to information in the indexes. It's also possible to weight by date because each page has a modification time. Most search engines support the use of the boolean operators AND, OR and NOT to help end users refine the search query. Boolean operators are for literal searches that allow the user to refine and extend the terms of the search. The engine looks for the words or phrases exactly as entered. Some search engines provide an advanced feature called proximity search, which allows users to define the distance between keywords.

Local search

Local search is the process that optimizes the efforts of local businesses. They focus on change to make sure all searches are consistent. It's important because many people determine where they plan to go and what to buy based on their searches.

Market share 

Google is by far the world's most used search engine, with a market share of 92.01%, and the world's other most used search engines were Bing, Yahoo!, Baidu, Yandex, and DuckDuckGo.

There are basically three types of search engines: Those that are powered by robots and those that are powered by human submissions; and those that are a hybrid of the two.

Crawler-based search engines are those that use automated software agents that visit a Web site, read the information on the actual site, read the site's meta tags and also follow the links that the site connects to performing indexing on all linked Web sites as well. The crawler returns all that information back to a central depository, where the data is indexed. The crawler will periodically return to the sites to check for any information that has changed. The frequency with which this happens is determined by the administrators of the search engine.

Human-powered search engines rely on humans to submit information that is subsequently indexed and catalogued. Only information that is submitted is put into the index.

In both cases, when you query a search engine to locate information, you're actually searching through the index that the search engine has created —you are not actually searching the Web. These indices are giant databases of information that is collected and stored and subsequently searched. This explains why sometimes a search on a commercial search engine, such as Yahoo! or Google, will return results that are, in fact, dead links. Since the search results are based on the index, if the index has not been updated since a Web page became invalid the search engine treats the page as still an active link even though it no longer is. It will remain that way until the index is updated.

So why will the same search on different search engines produce different results? Part of the answer to that question is because not all indices are going to be exactly the same. It depends on what the spiders find or what the humans submitted. But more important, not every search engine uses the same algorithm to search through the indices. The algorithm is what the search engines use to determine the relevance of the information in the index to what the user is searching for.

One of the elements that a search engine algorithm scans for is the frequency and location of keywords on a Web page. Those with higher frequency are typically considered more relevant. But search engine technology is becoming sophisticated in its attempt to discourage what is known as keyword stuffing, or spamdexing

Another common element that algorithms analyze is the way that pages link to other pages in the Web. By analyzing how pages link to each other, an engine can both determine what a page is about and whether that page is considered "important" and deserving of a boost in ranking. Just as the technology is becoming increasingly sophisticated to ignore keyword stuffing, it is also becoming more savvy to Web masters who build artificial links into their sites in order to build an artificial ranking.

Modern web search engines are highly intricate software systems that employ technology that has evolved over the years. There are a number of sub-categories of search engine software that are separately applicable to specific 'browsing' needs. These include web search engines, database or structured data search engines, and mixed search engines or enterprise search. The more prevalent search engines, such as Google and Yahoo!, utilize hundreds of thousands computers to process trillions of web pages in order to return fairly well-aimed results. Due to this high volume of queries and text processing, the software is required to run in a highly dispersed environment with a high degree of superfluity.

Another category of search engines is scientific search engines. These are search engines which search scientific literature. Best known example is GoogleScholar. Researchers are working on improve search engine technology by making the engines understand the content element of the articles, such as extracting theoretical constructs or key research findings.