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Delivers real-time results even for large datasets Conventional "fuzzy matching" technology is computer-resource intensive: rule sets often drive multiple queries on databases, as developers compensate for the lack of sophisticated matching technology using multiple "wildcard" searches, or testing validity of error-prone algorithms such as Soundex. More sophisticated algorithms such as "Edit Distance" are highly computationally intensive. Computation increases exponentially with the size of the "edit window" and the number of records being matched. These limitations make "fuzzy matching" unusuable in many applications. You can accept serious constraints on the number of records tested, or the matching window applied, and risk missing matches. Alternatively, you can accept batch processing, and risk being late with the right answer. Or you can deploy Netrics. Netrics' patented bi-partite graph technology is inherently efficient, and scales linearly with the number of records in the database. The technology has been proven in real-time applications for databases as large as 500 million records. Netrics' embeddable architecture operates independently of your existing applications and DBMS. Many customers actually experience a DECREASE in computer resources required when the fuzzy matching is shifted from the DBMS (which is optimized for exact matching) onto the Netrics processor. . .
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