Search and text analytics systems traditionally work on “flattened” data and information in which linked data is collated at the document level. A search system typically receives a query and executes the query to identify search results, such as documents. The search results resolve to the document level, and facets (dimensions) may be used to navigate or drill-down to select narrower results from current available search results as constrained by the query in effect and other selection criteria.
A text analytics system typically analyzes text in documents to generate information for analysis (e.g., with lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation identification, information extraction, etc.). Text analytics may also be referred to as data mining and may include performing link and association analysis and drill down.
Sometimes, relational data is also included in a search or text mining collection, but the relationships between information may be flattened (lost) in order to conform to a simple document model. If a user wants to use such relationship information stored in a search engine's flattened documents, and drill down into linked information, a user may look at the metadata of a document in the search results, select a field containing a key into the “relational” data, clear the current query and search criteria, and issue a new query using the key in order to view the related data in the search results or to navigate to individual related data documents.