Database search engine: Difference between revisions

Revision as of 01:09, 23 June 2015

Revision as of 13:45, 26 June 2015

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Searching for text-based content in [[databases]] or [[structured data]] formats (such as [[XML]] and [[Comma-separated values|CSV]]) 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. However databases allow logical queries, such as the use of multi-field [[Boolean logic]], while full-text searches do 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 are usually included with major database software products. As such, they are commonly called "indexing engines". However, these indexing engines are relatively limited in their ability to customize indexing formats (such as compounding, normalization, transformation and [[transliteration]].). Usually, they do not provide sophisticated data-matching technology (such as [[string matching]], [[Boolean logic]], algorithmic methods and search scripting).

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.