How does search engine perform?-SEO

Search engines are programs which search documents,websites for specified keywords and returns a list of it where those keywords were found. The term is often used to specifically describe systems like Google, Bing and Yahoo!

That's why it is very important to target right keywords for your website, create valuable backlinks and make sure content corresponds to keywords.

SEO works with the data you wish you find (words, behaviour, products).

It defines the users and helps you reach out to the people you're looking for.

Few Essentials to perform SEO on any Website

• Content: Is determined by the theme that is being given, the text on the page, and the titles and descriptions that are given.
• Performance: How fast is your site and does it work properly?
• User Experience: How does the site look? Is it easy to navigate around? Does it look safe? 

My Opinion on behalf of the latest's updates  Search engines how to optimize and how defined ranking criteria of website on google.

1. website speed performance.
2. website front-end coding.
3. Mobile Friendly Structure
4. Friendly User Experience
5. Content Quality (Content is King for Search Engines) [Major Point]
6. Your Social Media Presence.
7. secure sockets layer Certificate (SSL) is a plus point
8.  And your Online Presence.
9. White hat OFF page Strategies.

 There are many more Facts that increase ranking and give positive Indication to Search engine for improving your ranking.

if do you have more ideas please share with Marketing Community.

Search engine is the popular term for an information retrieval (IR) system. While researchers and developers take a broader view of IR systems, consumers think of them more in terms of what they want the systems to do — namely search the Web, or an intranet, or a database. Actually consumers would really prefer a finding engine, rather than a search engine.

Search engines match queries against an index that they create. The index consists of the words in each document, plus pointers to their locations within the documents. This is called an inverted file. A search engine or IR system comprises four essential modules:

• A document processor
• A query processor
• A search and matching function
• A ranking capability

While users focus on "search," the search and matching function is only one of the four modules. Each of these four modules may cause the expected or unexpected results that consumers get when they use a search engine.

  Document Processor

  The document processor prepares, processes, and inputs the documents, pages, or sites that users search against. The document processor performs some or all of the following steps:

• Normalizes the document stream to a predefined format.
• Breaks the document stream into desired retrievable units.
• Isolates and metatags subdocument pieces.
• Identifies potential indexable elements in documents.
• Deletes stop words.
• Stems terms.
• Extracts index entries.
• Computes weights.
• Creates and updates the main inverted file against which the search engine searches in order to match queries to documents.

Steps1-3: Preprocessing. While essential and potentially important in affecting the outcome of a search, these first three steps simply standardize the multiple formats encountered when deriving documents from various providers or handling various Web sites. The steps serve to merge all the data into a single consistent data structure that all the downstream processes can handle. The need for a well-formed, consistent format is of relative importance in direct proportion to the sophistication of later steps of document processing. Step two is important because the pointers stored in the inverted file will enable a system to retrieve various sized units — either site, page, document, section, paragraph, or sentence.

Step4: Identify elements to index. Identifying potential indexable elements in documents dramatically affects the nature and quality of the document representation that the engine will search against. In designing the system, we must define the word "term." Is it the alpha-numeric characters between blank spaces or punctuation? If so, what about non-compositional phrases (phrases in which the separate words do not convey the meaning of the phrase, like "skunk works" or "hot dog"), multi-word proper names, or inter-word symbols such as hyphens or apostrophes that can denote the difference between "small business men" versus small-business men." Each search engine depends on a set of rules that its document processor must execute to determine what action is to be taken by the "tokenizer," i.e. the software used to define a term suitable for indexing.

Step5: Deleting stop words. This step helps save system resources by eliminating from further processing, as well as potential matching, those terms that have little value in finding useful documents in response to a customer's query. This step used to matter much more than it does now when memory has become so much cheaper and systems so much faster, but since stop words may comprise up to percent of text words in a document, it still has some significance. A stop word list typically consists of those word classes known to convey little substantive meaning, such as articles (a, the), conjunctions (and, but), interjections (oh, but), prepositions (in, over), pronouns (he, it), and forms of the "to be" verb (is, are). To delete stop words, an algorithm compares index term candidates in the documents against a stop word list and eliminates certain terms from inclusion in the index for searching.

Step6: Term Stemming. Stemming removes word suffixes, perhaps recursively in layer after layer of processing. The process has two goals. In terms of efficiency, stemming reduces the number of unique words in the index, which in turn reduces the storage space required for the index and speeds up the search process. In terms of effectiveness, stemming improves recall by reducing all forms of the word to a base or stemmed form. For example, if a user asks for analyze, they may also want documents which contain analysis, analyzing, analyzer, analyzes, and analyzed. Therefore, the document processor stems document terms to analysis- so that documents which include various forms of analysis- will have equal likelihood of being retrieved; this would not occur if the engine only indexed variant forms separately and required the user to enter all. Of course, stemming does have a downside. It may negatively affect precision in that all forms of a stem will match, when, in fact, a successful query for the user would have come from matching only the word form actually used in the query.

Systems may implement either a strong stemming algorithm or a weak stemming algorithm. A strong stemming algorithm will strip off both inflectional suffixes (-s, -es, -ed) and derivational suffixes (-able, -aciousness, -ability), while a weak stemming algorithm will strip off only the inflectional suffixes (-s, -es, -ed).

Step7: Extract index entries. Having completed steps1 through6, the document processor extracts the remaining entries from the original document. For example, the following paragraph shows the full text sent to a search engine for processing:

Milosevic's comments, carried by the official news agency Tanjug, cast doubt over the governments at the talks, which the international community has called to try to prevent an all-out war in the Serbian province. "President Milosevic said it was well known that Serbia and Yugoslavia were firmly committed to resolving problems in Kosovo, which is an integral part of Serbia, peacefully in Serbia with the participation of the representatives of all ethnic communities," Tanjug said. Milosevic was speaking during a meeting with British Foreign Secretary Robin Cook, who delivered an ultimatum to attend negotiations in a week's time on an autonomy proposal for Kosovo with ethnic Albanian leaders from the province. Cook earlier told a conference that Milosevic had agreed to study the proposal.

Steps1 to6 reduce this text for searching to the following:

The output of step7 is then inserted and stored in an inverted file that lists the index entries and an indication of their position and frequency of occurrence. The specific nature of the index entries, however, will vary based on the decision in Step4 concerning what constitutes an "indexable term." More sophisticated document processors will have phrase recognizers, as well as Named Entity recognizers and Categorizers, to insure index entries such as Milosevic are tagged as a Person and entries such as Yugoslavia and Serbia as Countries.

Step8: Term weight assignment. Weights are assigned to terms in the index file. The simplest of search engines just assign a binary weight:1 for presence and0 for absence. The more sophisticated the search engine, the more complex the weighting scheme. Measuring the frequency of occurrence of a term in the document creates more sophisticated weighting, with length-normalization of frequencies still more sophisticated. Extensive experience in information retrieval research over many years has clearly demonstrated that the optimal weighting comes from use of "tf/idf." This algorithm measures the frequency of occurrence of each term within a document. Then it compares that frequency against the frequency of occurrence in the entire database.

Not all terms are good "discriminators" — that is, all terms do not single out one document from another very well. A simple example would be the word "the." This word appears in too many documents to help distinguish one from another. A less obvious example would be the word "antibiotic." In a sports database when we compare each document to the database as a whole, the term "antibiotic" would probably be a good discriminator among documents, and therefore would be assigned a high weight. Conversely, in a database devoted to health or medicine, "antibiotic" would probably be a poor discriminator, since it occurs very often. The TF/IDF weighting scheme assigns higher weights to those terms that really distinguish one document from the others.

Step9: Create index. The index or inverted file is the internal data structure that stores the index information and that will be searched for each query. Inverted files range from a simple listing of every alpha-numeric sequence in a set of documents/pages being indexed along with the overall identifying numbers of the documents in which the sequence occurs, to a more linguistically complex list of entries, the tf/idf weights, and pointers to where inside each document the term occurs. The more complete the information in the index, the better the search results. 

Query Processor.

Query processing has seven possible steps, though a system can cut these steps short and proceed to match the query to the inverted file at any of a number of places during the processing. Document processing shares many steps with query processing. More steps and more documents make the process more expensive for processing in terms of computational resources and responsiveness. However, the longer the wait for results, the higher the quality of results. Thus, search system designers must choose what is most important to their users — time or quality. Publicly available search engines usually choose time over very high quality, having too many documents to search against.

All about keyword are relevant what they needs, and make it priority depend how to make it

According to the most searched tags of visitors