{"id":160253,"date":"2010-04-01T00:00:00","date_gmt":"2010-04-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/fast-pseudo-random-fingerprints\/"},"modified":"2018-10-16T20:07:39","modified_gmt":"2018-10-17T03:07:39","slug":"fast-pseudo-random-fingerprints","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/fast-pseudo-random-fingerprints\/","title":{"rendered":"Fast Pseudo-Random Fingerprints"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We propose a method to exponentially speed up computation of various fingerprints, such as the ones used to compute similarity and rarity in massive data sets. Rather then maintaining the full stream of <i>b<\/i> items of a universe <i>[u]<\/i>, such methods only maintain a concise fingerprint of the stream, and perform computations using the fingerprints. The computations are done approximately, and the required fingerprint size <i>k<\/i> depends on the desired accuracy <i>&epsilon;<\/i> and confidence <i>&delta;<\/i>. Our technique maintains a single bit per hash function, rather than a single integer, thus requiring a fingerprint of length <i>k = O(frac ln  frac 1&delta;&epsilon;<sup>2<\/sup>)<\/i> bits, rather than <i>O(log  u \u00b7 frac ln  frac 1&delta;&epsilon;<sup>2<\/sup>)<\/i> bits required by previous approaches. The main advantage of the fingerprints we propose is that rather than computing the fingerprint of a stream of <i>b<\/i> items in time of <i>O(b \u00b7 k)<\/i>, we can compute it in time <i>O(b log  k)<\/i>. Thus this allows an exponential speedup for the fingerprint construction, or alternatively allows achieving a much higher accuracy while preserving computation time. Our methods rely on a specific family of pseudo-random hashes for which we can quickly locate hashes resulting in small values.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose a method to exponentially speed up computation of various fingerprints, such as the ones used to compute similarity and rarity in massive data sets. Rather then maintaining the full stream of b items of a universe [u], such methods only maintain a concise fingerprint of the stream, and perform computations using the fingerprints. 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