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Hideo Bannai

Publications and source records attributed to Hideo Bannai.

At least 73 records · Page 4Linked to original sources
arXiv2018

$O(n \log n)$-time text compression by LZ-style longest first substitution

Mauer et al. [A Lempel-Ziv-style Compression Method for Repetitive Texts, PSC 2017] proposed a hybrid text compression method called LZ-LFS which has both features of Lempel-Ziv 77 factorization and longest first substitution. They showed that LZ-LFS can achieve better compression ratio for repetitive texts, compared to some state-of-the-art compression algorithms. The drawback of Mauer et al.'s method is that their LZ-LFS compression algorithm takes $O(n^2)$ time on an input string of length $n$. In this paper, we show a faster LZ-LFS compression algorithm that works in $O(n \log n)$ time. We also propose a simpler version of LZ-LFS that can be computed in $O(n)$ time.

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arXiv2018

Computing Abelian regularities on RLE strings

Two strings x and y are said to be Abelian equivalent if x is a permutation of y, or vice versa. If a string z satisfies z = xy with x and y being Abelian equivalent, then z is said to be an Abelian square. If a string w can be factorized into a sequence v_1,...,v_s of strings such that v_1 ,..., v_{s-1} are all Abelian equivalent and vs is a substring of a permutation of v_1, then w is said to have a regular Abelian period (p,t) where p = |v_1| and t = |v_s|. If a substring w_1[i..i+l-1] of a string w_1 and a substring w_2[j..j+l-1] of another string w_2 are Abelian equivalent, then the substrings are said to be a common Abelian factor of w_1 and w_2 and if the length l is the maximum of such then the substrings are said to be a longest common Abelian factor of w_1 and w_2. We propose efficient algorithms which compute these Abelian regularities using the run length encoding (RLE) of strings. For a given string w of length n whose RLE is of size m, we propose algorithms which compute all Abelian squares occurring in w in O(mn) time, and all regular Abelian periods of w in O(mn) time. For two given strings w_1 and w_2 of total length n and of total RLE size m, we propose an algorithm which computes all longest common Abelian factors in O(m^2n) time.

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arXiv2017

Order preserving pattern matching on trees and DAGs

The order preserving pattern matching (OPPM) problem is, given a pattern string $p$ and a text string $t$, find all substrings of $t$ which have the same relative orders as $p$. In this paper, we consider two variants of the OPPM problem where a set of text strings is given as a tree or a DAG. We show that the OPPM problem for a single pattern $p$ of length $m$ and a text tree $T$ of size $N$ can be solved in $O(m+N)$ time if the characters of $p$ are drawn from an integer alphabet of polynomial size. The time complexity becomes $O(m \log m + N)$ if the pattern $p$ is over a general ordered alphabet. We then show that the OPPM problem for a single pattern and a text DAG is NP-complete.

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arXiv2017

Shortest unique palindromic substring queries in optimal time

A palindrome is a string that reads the same forward and backward. A palindromic substring $P$ of a string $S$ is called a shortest unique palindromic substring ($\mathit{SUPS}$) for an interval $[x, y]$ in $S$, if $P$ occurs exactly once in $S$, this occurrence of $P$ contains interval $[x, y]$, and every palindromic substring of $S$ which contains interval $[x, y]$ and is shorter than $P$ occurs at least twice in $S$. The $\mathit{SUPS}$ problem is, given a string $S$, to preprocess $S$ so that for any subsequent query interval $[x, y]$ all the $\mathit{SUPS}\mbox{s}$ for interval $[x, y]$ can be answered quickly. We present an optimal solution to this problem. Namely, we show how to preprocess a given string $S$ of length $n$ in $O(n)$ time and space so that all $\mathit{SUPS}\mbox{s}$ for any subsequent query interval can be answered in $O(k+1)$ time, where $k$ is the number of outputs.

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arXiv2017

Faster STR-IC-LCS computation via RLE

The constrained LCS problem asks one to find a longest common subsequence of two input strings $A$ and $B$ with some constraints. The STR-IC-LCS problem is a variant of the constrained LCS problem, where the solution must include a given constraint string $C$ as a substring. Given two strings $A$ and $B$ of respective lengths $M$ and $N$, and a constraint string $C$ of length at most $\min\{M, N\}$, the best known algorithm for the STR-IC-LCS problem, proposed by Deorowicz~({\em Inf. Process. Lett.}, 11:423--426, 2012), runs in $O(MN)$ time. In this work, we present an $O(mN + nM)$-time solution to the STR-IC-LCS problem, where $m$ and $n$ denote the sizes of the run-length encodings of $A$ and $B$, respectively. Since $m \leq M$ and $n \leq N$ always hold, our algorithm is always as fast as Deorowicz's algorithm, and is faster when input strings are compressible via RLE.

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arXiv2017

Small-space encoding LCE data structure with constant-time queries

The \emph{longest common extension} (\emph{LCE}) problem is to preprocess a given string $w$ of length $n$ so that the length of the longest common prefix between suffixes of $w$ that start at any two given positions is answered quickly. In this paper, we present a data structure of $O(z τ^2 + \frac{n}τ)$ words of space which answers LCE queries in $O(1)$ time and can be built in $O(n \log σ)$ time, where $1 \leq τ\leq \sqrt{n}$ is a parameter, $z$ is the size of the Lempel-Ziv 77 factorization of $w$ and $σ$ is the alphabet size. This is an \emph{encoding} data structure, i.e., it does not access the input string $w$ when answering queries and thus $w$ can be deleted after preprocessing. On top of this main result, we obtain further results using (variants of) our LCE data structure, which include the following: - For highly repetitive strings where the $zτ^2$ term is dominated by $\frac{n}τ$, we obtain a \emph{constant-time and sub-linear space} LCE query data structure. - Even when the input string is not well compressible via Lempel-Ziv 77 factorization, we still can obtain a \emph{constant-time and sub-linear space} LCE data structure for suitable $τ$ and for $σ\leq 2^{o(\log n)}$. - The time-space trade-off lower bounds for the LCE problem by Bille et al. [J. Discrete Algorithms, 25:42-50, 2014] and by Kosolobov [CoRR, abs/1611.02891, 2016] can be "surpassed" in some cases with our LCE data structure.

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arXiv2017

Tight bounds on the maximum number of shortest unique substrings

A substring Q of a string S is called a shortest unique substring (SUS) for interval [s,t] in S, if Q occurs exactly once in S, this occurrence of Q contains interval [s,t], and every substring of S which contains interval [s,t] and is shorter than Q occurs at least twice in S. The SUS problem is, given a string S, to preprocess S so that for any subsequent query interval [s,t] all the SUSs for interval [s,t] can be answered quickly. When s = t, we call the SUSs for [s,t] as point SUSs, and when s \leq t, we call the SUSs for [s,t] as interval SUSs. There exist optimal O(n)-time preprocessing scheme which answers queries in optimal O(k) time for both point and interval SUSs, where n is the length of S and k is the number of outputs for a given query. In this paper, we reveal structural, combinatorial properties underlying the SUS problem: Namely, we show that the number of intervals in S that correspond to point SUSs for all query positions in S is less than 1.5n, and show that this is a matching upper and lower bound. Also, we consider the maximum number of intervals in S that correspond to interval SUSs for all query intervals in S.

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arXiv2017

Computing All Distinct Squares in Linear Time for Integer Alphabets

Given a string on an integer alphabet, we present an algorithm that computes the set of all distinct squares belonging to this string in time linear to the string length. As an application, we show how to compute the tree topology of the minimal augmented suffix tree in linear time. Asides from that, we elaborate an algorithm computing the longest previous table in a succinct representation using compressed working space.

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arXiv2017

Longest Common Subsequence in at Least $k$ Length Order-Isomorphic Substrings

We consider the longest common subsequence (LCS) problem with the restriction that the common subsequence is required to consist of at least $k$ length substrings. First, we show an $O(mn)$ time algorithm for the problem which gives a better worst-case running time than existing algorithms, where $m$ and $n$ are lengths of the input strings. Furthermore, we mainly consider the LCS in at least $k$ length order-isomorphic substrings problem. We show that the problem can also be solved in $O(mn)$ worst-case time by an easy-to-implement algorithm.

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arXiv2016

Dynamic index and LZ factorization in compressed space

In this paper, we propose a new \emph{dynamic compressed index} of $O(w)$ space for a dynamic text $T$, where $w = O(\min(z \log N \log^*M, N))$ is the size of the signature encoding of $T$, $z$ is the size of the Lempel-Ziv77 (LZ77) factorization of $T$, $N$ is the length of $T$, and $M \geq 3N$ is an integer that can be handled in constant time under word RAM model. Our index supports searching for a pattern $P$ in $T$ in $O(|P| f_{\mathcal{A}} + \log w \log |P| \log^* M (\log N + \log |P| \log^* M) + \mathit{occ} \log N)$ time and insertion/deletion of a substring of length $y$ in $O((y+ \log N\log^* M)\log w \log N \log^* M)$ time, where $f_{\mathcal{A}} = O(\min \{ \frac{\log\log M \log\log w}{\log\log\log M}, \sqrt{\frac{\log w}{\log\log w}} \})$. Also, we propose a new space-efficient LZ77 factorization algorithm for a given text of length $N$, which runs in $O(N f_{\mathcal{A}} + z \log w \log^3 N (\log^* N)^2)$ time with $O(w)$ working space.

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arXiv2016

Fully dynamic data structure for LCE queries in compressed space

A Longest Common Extension (LCE) query on a text $T$ of length $N$ asks for the length of the longest common prefix of suffixes starting at given two positions. We show that the signature encoding $\mathcal{G}$ of size $w = O(\min(z \log N \log^* M, N))$ [Mehlhorn et al., Algorithmica 17(2):183-198, 1997] of $T$, which can be seen as a compressed representation of $T$, has a capability to support LCE queries in $O(\log N + \log \ell \log^* M)$ time, where $\ell$ is the answer to the query, $z$ is the size of the Lempel-Ziv77 (LZ77) factorization of $T$, and $M \geq 4N$ is an integer that can be handled in constant time under word RAM model. In compressed space, this is the fastest deterministic LCE data structure in many cases. Moreover, $\mathcal{G}$ can be enhanced to support efficient update operations: After processing $\mathcal{G}$ in $O(w f_{\mathcal{A}})$ time, we can insert/delete any (sub)string of length $y$ into/from an arbitrary position of $T$ in $O((y+ \log N\log^* M) f_{\mathcal{A}})$ time, where $f_{\mathcal{A}} = O(\min \{ \frac{\log\log M \log\log w}{\log\log\log M}, \sqrt{\frac{\log w}{\log\log w}} \})$. This yields the first fully dynamic LCE data structure. We also present efficient construction algorithms from various types of inputs: We can construct $\mathcal{G}$ in $O(N f_{\mathcal{A}})$ time from uncompressed string $T$; in $O(n \log\log n \log N \log^* M)$ time from grammar-compressed string $T$ represented by a straight-line program of size $n$; and in $O(z f_{\mathcal{A}} \log N \log^* M)$ time from LZ77-compressed string $T$ with $z$ factors. On top of the above contributions, we show several applications of our data structures which improve previous best known results on grammar-compressed string processing.

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arXiv2016

Dynamic index, LZ factorization, and LCE queries in compressed space

In this paper, we present the following results: (1) We propose a new \emph{dynamic compressed index} of $O(w)$ space, that supports searching for a pattern $P$ in the current text in $O(|P| f(M,w) + \log w \log |P| \log^* M (\log N + \log |P| \log^* M) + \mathit{occ} \log N)$ time and insertion/deletion of a substring of length $y$ in $O((y+ \log N\log^* M)\log w \log N \log^* M)$ time, where $N$ is the length of the current text, $M$ is the maximum length of the dynamic text, $z$ is the size of the Lempel-Ziv77 (LZ77) factorization of the current text, $f(a,b) = O(\min \{ \frac{\log\log a \log\log b}{\log\log\log a}, \sqrt{\frac{\log b}{\log\log b}} \})$ and $w = O(z \log N \log^*M)$. (2) We propose a new space-efficient LZ77 factorization algorithm for a given text of length $N$, which runs in $O(N f(N,w') + z \log w' \log^3 N (\log^* N)^2)$ time with $O(w')$ working space, where $w' =O(z \log N \log^* N)$. (3) We propose a data structure of $O(w)$ space which supports longest common extension (LCE) queries on the text in $O(\log N + \log \ell \log^* N)$ time, where $\ell$ is the output LCE length. On top of the above contributions, we show several applications of our data structures which improve previous best known results on grammar-compressed string processing.

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arXiv2016

Deterministic sub-linear space LCE data structures with efficient construction

Given a string $S$ of $n$ symbols, a longest common extension query $\mathsf{LCE}(i,j)$ asks for the length of the longest common prefix of the $i$th and $j$th suffixes of $S$. LCE queries have several important applications in string processing, perhaps most notably to suffix sorting. Recently, Bille et al. (J. Discrete Algorithms 25:42-50, 2014, Proc. CPM 2015: 65-76) described several data structures for answering LCE queries that offers a space-time trade-off between data structure size and query time. In particular, for a parameter $1 \leq τ\leq n$, their best deterministic solution is a data structure of size $O(n/τ)$ which allows LCE queries to be answered in $O(τ)$ time. However, the construction time for all deterministic versions of their data structure is quadratic in $n$. In this paper, we propose a deterministic solution that achieves a similar space-time trade-off of $O(τ\min\{\logτ,\log\frac{n}τ\})$ query time using $O(n/τ)$ space, but significantly improve the construction time to $O(nτ)$.

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arXiv2015

The "Runs" Theorem

We give a new characterization of maximal repetitions (or runs) in strings based on Lyndon words. The characterization leads to a proof of what was known as the "runs" conjecture (Kolpakov \& Kucherov (FOCS '99)), which states that the maximum number of runs $ρ(n)$ in a string of length $n$ is less than $n$. The proof is remarkably simple, considering the numerous endeavors to tackle this problem in the last 15 years, and significantly improves our understanding of how runs can occur in strings. In addition, we obtain an upper bound of $3n$ for the maximum sum of exponents $σ(n)$ of runs in a string of length $n$, improving on the best known bound of $4.1n$ by Crochemore et al. (JDA 2012), as well as other improved bounds on related problems. The characterization also gives rise to a new, conceptually simple linear-time algorithm for computing all the runs in a string. A notable characteristic of our algorithm is that, unlike all existing linear-time algorithms, it does not utilize the Lempel-Ziv factorization of the string. We also establish a relationship between runs and nodes of the Lyndon tree, which gives a simple optimal solution to the 2-Period Query problem that was recently solved by Kociumaka et al. (SODA 2015).

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arXiv2015

Constructing LZ78 Tries and Position Heaps in Linear Time for Large Alphabets

We present the first worst-case linear-time algorithm to compute the Lempel-Ziv 78 factorization of a given string over an integer alphabet. Our algorithm is based on nearest marked ancestor queries on the suffix tree of the given string. We also show that the same technique can be used to construct the position heap of a set of strings in worst-case linear time, when the set of strings is given as a trie.

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arXiv2013

Space Efficient Linear Time Lempel-Ziv Factorization on Constant~Size~Alphabets

We present a new algorithm for computing the Lempel-Ziv Factorization (LZ77) of a given string of length $N$ in linear time, that utilizes only $N\log N + O(1)$ bits of working space, i.e., a single integer array, for constant size integer alphabets. This greatly improves the previous best space requirement for linear time LZ77 factorization (Kärkkäinen et al. CPM 2013), which requires two integer arrays of length $N$. Computational experiments show that despite the added complexity of the algorithm, the speed of the algorithm is only around twice as slow as previous fastest linear time algorithms.

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arXiv2013

Time and Space Efficient Lempel-Ziv Factorization based on Run Length Encoding

We propose a new approach for calculating the Lempel-Ziv factorization of a string, based on run length encoding (RLE). We present a conceptually simple off-line algorithm based on a variant of suffix arrays, as well as an on-line algorithm based on a variant of directed acyclic word graphs (DAWGs). Both algorithms run in $O(N+n\log n)$ time and O(n) extra space, where N is the size of the string, $n\leq N$ is the number of RLE factors. The time dependency on N is only in the conversion of the string to RLE, which can be computed very efficiently in O(N) time and O(1) extra space (excluding the output). When the string is compressible via RLE, i.e., $n = o(N)$, our algorithms are, to the best of our knowledge, the first algorithms which require only o(N) extra space while running in $o(N\log N)$ time.

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