Database search engine: Difference between revisions

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== Modern search engine components ==
 
Searching for text-based content in [[databases]] or [[structured data]] formats (such as [[XML]], [[Comma-separated values|CSV]], etc.) presents special challenges and opportunities, which specialized search engines resolve. Databases are slow when solving complex queries, which have multiple logical or [[string matching]] arguments. DatabasesHowever databases allow logical queries, such as the use of multi-field [[booleanBoolean logic]], whichwhile full-text searches don'tdo not. "Crawling" (a human by-eye search) is not necessary to find information stored in a database, since the data is already structured, although it is often necessary to index the data in a more compact form that is designed to allow for faster searches.
 
Database search engines were initially (and stillare usually are) included with major database software products. As such, they are usuallycommonly called "indexing engines". However, these indexing engines are relatively limited in their ability to customize indexing formats (such as compounding, normalization, transformation, and [[transliteration]], etc.). Usually, they do not provide sophisticated data-matching technology (such as [[string matching]], [[booleanBoolean logic]], algorithmic methods, and search scripting, etc.).
 
In more advanced database search systems, relational databases are indexed by compounding multiple tables into a single table containing only the fields that need to be "queried" (displayed in search results). The actual data-matching engines can include a multitude of functions, including basic string matching, normalization and transformation. Database search technology is used heavily by many large public and private entities, including government database services, e-commerce companies, online advertising platforms, telecommunications service providers and other consumers with a need to access information in large repositories.
 
==See also==