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By clustering groups of near-duplicate documents, Equivio helps intelligence organizations reduce data review effort. Equivio breaks large collections of documents, messages and emails into sets of near-duplicates. This radically reduces complexity, allowing the analyst to rapidly home in on the most relevant, critical information. The resultant review process takes less time, costs less, and there is less risk of analytical error and oversight.

Equivio helps analysts make sense of large sets of documents, while enhancing the quality of analysis. Typical usage scenarios include ongoing scanning and distribution operations, as well as longer-term research assignments.

Equivio is also used to support text mining. By detecting near-duplicates, Equivio enables the reduction of the input set. This helps ensure completion of text mining processing in the time window, while also eliminating skewed results which can derive from redundancies in the source data.
 

Benefits
 
In time-critical data scanning scenarios, the grouping of near-duplicates enables the prioritization of document review - for example, where time does not allow a full review of all documents, the analyst might decide to read only a sample document from each near-duplicate set, or to read one document and then review differences.
   
By clustering near-duplicates, Equivio enables a more systematic review process; for example, by ensuring that a set of similar documents is assigned to and reviewed by one analyst at a single point in time. This helps the analyst develop a coherent picture of these documents, focusing on the value-added information in each document, and reducing errors due to oversights of key data.
   
Equivio's ability to group near-duplicates introduces a dimension of structure into an unstructured document repository. This helps the intelligence organization cope with data sets that otherwise may be too obscure or complex to yield analytical value.
   
For incoming documents, Equivio can identify whether a similar document has previously been received by the organization. This information helps analysts decide which documents to distribute and to whom. In addition, the incoming document can be annotated with the near-duplicate information, facilitating a more efficient and complete review process downstream.
   
In text mining scenarios, Equivio's ability to identify near-duplicates is used to eliminate data redundancies in the input set. This ensures the completion of processing in the given time window, while also ensuring more accurate analysis and conclusions.
   
   
 
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