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Deep Search, Wide Search, & Everything Else You Should Know About Semantic Search

By   /  April 23, 2014  /  No Comments

Dick Bourke of Engineering.com recently shared a primer on current search and discovery solutions.


Dick Bourke of Engineering.com recently shared a primer on current search and discovery solutions. He writes, “I’ve written several posts describing Search & Discovery Solutions (SDSs). This post will pull them together in an overview… The core of an SDS is a search engine that indexes and searches a wide range of product data. Search engines operate with an index – an optimized file format that supports rapid data access and display of search results. They usually do not store the complete sources. A search engine must give fast results. For example, one hi-tech manufacturing company reports that search results display in less than two seconds when accessing 16 million items and metadata from within 24 million documents, drawings and images. The usual approach to SDS indexing is based on text values. This is in stark contrast to relational databases that store data in tables, records, attributes and values. What’s more, an index eliminates any need for an intermediate relational database to help with queries.”


Bourke goes on to discuss Deep Search: “With full text capability: ‘The search engine examines all words/objects in every stored document as it tries to match the search criteria entered by the user. This distinguishes it from searches based on metadata or parts of the original texts represented in databases, such as titles or selected sections.’ – Wikipedia. Full text does not rely on consistent metadata; it drives through metadata to access all relevant product data. In contrast, a real “gotcha” with a relational database query is that it relies on consistent metadata – a highly questionable assumption. Put another way, do you trust your company’s metadata? Deep, full text searching gains significant benefits – among them: (1) Negating the need for users to remember where the needed data is stored. (2) Avoiding costly reorganizing and migrating data to satisfy relational database retrievals.”


Read more here.


Image: Courtesy Flickr/ Ivy Dawned


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