Today’s intelligent search solutions must be built on architectures that can handle the performance demands of big data workloads. In the face of rapid growth in the volume and variety of data that enterprise search tools must examine, result retrieval speed has become a key indicator of cognitive search algorithm performance. However, the rise and popularization of free, publicly accessible web search engines, such as Google (and its predecessor AltaVista), radically transformed user expectations for information retrieval, content discovery and enterprise search platforms. With the growth of desktop computing and corporate intranets, commercial enterprise search solutions, such as the IBM Storage and Information Retrieval System (STAIRS) and the local search tool FAST (later acquired by Microsoft), became mainstream in enterprise computing.
One of the earliest benefits to implementing multi-user mainframe computer systems was that they facilitated information discovery by finding exact matches to text strings in large document repositories.
Enterprise information retrieval systems came into existence long before the public internet did.
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