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Intelligent Access to Large Collections of Amorphous Data

Approximate matching may be applied to address one of the most challenging problems of modern information technology how to extract meaning from large collections of data. By collecting similar codewords through the assignment of common hash indices, the suggested technique provides an extremely efficient clustering procedure. Leveraging this highly efficient technique shifts the problem of meaning extraction from the trial and error creation of complex comparison routines to the formulation of a suitable attribute template or templates defining appropriate clustering criteria. The proposed method of “meaning extraction” is based on the efficiency of the underlying clustering technique. Because of this efficiency, it becomes practical to evaluate the performance of a large number of potential templates. A template may then be selected to extract information that might otherwise be hidden within the collection of amorphous data. This hidden information or “meaning” of the data may be identified a posteriori by observation of the desired clustering, without necessarily having advance (i.e., a priori) knowledge or appreciation of the underlying clustering criteria. In contrast, prior techniques require detailed and extensive a priori knowledge of data meaning so as to formulate selection criteria to exactly match and thereby identify items. Even once identified, prior techniques must perform an arduous comparison between each item and all others to identify clusters