Quality-performance optimized identification of duplicate data
Abstract:
An approach is provided for providing optimized identification of duplicate data in a networked computing environment. An aggregate feature vector is created that is specific to an attribute of the data (e.g., a field that holds specific informational content). The aggregate feature vector has a set of dimensions that each define a specific comparison function used to test for similarity between data entries in the attribute. Each dimension in the aggregate feature vector is assigned an effectiveness, and a cost is computed for each dimension. Based on these two, a subset of dimensions is selected to form an optimized feature vector. This optimized feature vector can then be used to analyze a dataset to find matching data.
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