Range of Search Engine Database | Digitalize Pedia

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Full-text search engine optimization: Full-text search engines fetch information from the web, build database, and get records based on user queries. the first one has its crawler. Another way is to always rent another search engine’s database. List search engine:

We can’t talk about a real search engine. It retrieves all directory listing entries. META search engine: It provides search results from multiple search engines simultaneously based on user queries. Vertical search engine:

Vertical search engines focus on a particular search domain and search query [2]. There are many search engines like Bing, AOL, Google, and Question, but the most popular one is Google. 

Regarding Examiner, over 807 first trips to a website came from internet research. And during those trips, more than 76% use Google’s global search engine. Furthermore, this implies that “84%” of Bing searchers never get ahead of the next page of search results, and 65% rarely click on paid or sponsored results. 

Therefore, getting the best search engine results is essential to having a steady flow of visitors to websites and this is where the value of search engine optimization comes into play. 

What’s Search Engine Optimization? 

In layman’s language of search engine optimization, when we type a question in a search engine and press the important enter key, we get a list of web pages as a result including the phrase from that question being asked. Customers often visit websites or websites that may be at the top of this list because they realize that they are relevant to the question being asked. 

If we have ever wondered why some of these websites rank better than others, we should know that it could be due to a great internet marketing method called Engine Optimization. 

Feature Of Search Engine Optimization

As mentioned earlier, each search engine can be characterized by the features they implement as well as the performance they have in different situations. We have identified 13 general characteristics that can be used to describe each search engine, based solely on the intrinsic features and characteristics they possess:

Storage Specifies how the indexer stores the index, using a database engine or a simple file structure (e.g. inverted index).

Incremental Index Indicates whether the indexer can add files to the existing index without having to rebuild the entire index.

Results Excerpt: If the tool gives a snippet with the results.

The Resulting Model: Some tools provide the ability to use a model to analyze the results of a query.

Stopwords: Indicates whether the indexer can use a list of words used as stopwords to filter out terms that are too frequent.

File Types File types that the indexer can crawl. The common file type of the analyzed tools is HTML.

Root Words Whether indexers/searchers can perform root word operations on words. Fuzzy Search Ability to solve queries in a fuzzy way, i.e. not necessarily an exact match to the query.

Sort Ability To sort results by several criteria. Rating Indicates whether the tool returns results based on the database of the rating function.

Search type The type of search it can perform and whether it accepts query operators.

Indexing Language The programming language used to perform the indexing. This information is useful for extending functionality or integrating it into an existing platform. 

License Defines the terms of use and modification of the indexer and/or search engine.

Description Each search engine to be analyzed can be briefly described, depending on who and where they developed it and the exact characteristics that define them.

htt://Dig  is a set of tools for indexing and searching a web page. It provides a command line tool to perform searches as well as a CGI interface. Although there are newer versions than the one used, according to their website version 3.1.6 is the fastest.

IXE Toolkit is a collection of modular C++ classes and utilities for indexing and querying documents. There is a commercial version from Tiscali (Italy), as well as a non-commercial version for academic purposes.

Indri is a search engine built on top of the Lemur project, which is a set of tools designed to study language modeling and information retrieval. This project was developed through collaborative work between the University of Massachusetts and Carnegie Mellon University, USA. 

Lucene is part of the Apache Software Foundation’s text search engine library. Since it’s a library, some applications use it, eg. the Nutch project. In current work, simple applications are provided with the library used to index the collection.

MG4J (Gigabyte Management for Java) is a full-text indexer for a large collection of documents, developed at the University of Milan, Italy. As a by-product, they provide general-purpose optimized classes for string processing, bitwise I/O, and more. 

Omega is an application built on Xapian, an open-source Probabilistic Information Retrieval library. Xapian is written in C++ but can link with different languages ​​(Perl, Python, PHP, Java, TCL, C#).

IBM Omnifind Yahoo! Version is a Research software that allows rapid deployment of intranet research. It combines internal search, based on the Lucene search engine, with the ability to search the Internet through Yahoo! search engine.

SWISH-E (Simple Web Indexing System for Humans – Enhanced) is an open-source tool for indexing and searching. This is an improved version of SWISH, written by Kevin Hughes.

SWISH++ is a search and indexing engine based on the database Swish-E, although completely rewritten in C++. It has most of the features of the Swish-E, but it doesn’t have all of them.

XMLSearch is a set of classes developed in C++ that allow indexing and searching in sets of documents, extending the search with text operators (equal, prefix, suffix, expression, etc.).