Abstract
Let s = s 1 .. s n be a text (or sequence) on a finite alphabet Σ. A fingerprint in s is the set of distinct characters contained in one of its substrings. Fingerprinting a text consists in computing the set \({\cal F}\) of all fingerprints of all its substrings. A fingerprint, \(f \in {\cal F}\), admits a number of maximal locations 〈i,j 〉 in S, that is the alphabet of s i .. s j is f and s i − 1, s j + 1, if defined, are not in f. The set of maximal locations is \({\cal L}, \; |{\cal L}| \leq n |\Sigma|.\) Two maximal locations 〈i,j 〉 and 〈k,l 〉 such that s i ..s j = s k ..s l are named copies and the quotient of \({\cal L}\) according to the copy relation is named \({\cal L}_C\). The faster algorithm to compute all fingerprints in s runs in \(O(n+|{\cal L}|\log |\Sigma|)\) time. We present an \(O((n+|{\cal L}_C|)\log |\Sigma|)\) worst case time algorithm.
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Kolpakov, R., Raffinot, M. (2008). Faster Text Fingerprinting. In: Amir, A., Turpin, A., Moffat, A. (eds) String Processing and Information Retrieval. SPIRE 2008. Lecture Notes in Computer Science, vol 5280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89097-3_4
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DOI: https://doi.org/10.1007/978-3-540-89097-3_4
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