METHOD FOR USING ONE-DIMENSIONAL DYNAMICS IN ASSESSING THE SIMILARITY OF SETS OF DATA

摘要

A method for finding sets of data (SDDS) for presentation in one-dimension, which are similar to a target SDD, is invented. The method leverages a new category of signatures, called equivalence signatures, to characterize the SDDs and is applicable to all types of data with special interpretation for data, such as text, binaries and audio, that may be presented in one-dimension. The equivalence signature is computed as the functional for the kinetic energy of a point particle whose path is specified by the values of the digital data. These signatures have the salient feature that, at worst, they change in a bounded manner when small changes are made to the SDDs and when used to find SDDs that are similar to a target SDDs, they allow for a significant reduction in the number of SDDs to be compared with the target. This is an improvement over the state of the art wherein the computational expensive process of performing a complete search against the entire corpus must be applied.