One of the most famous and investigated lossless data-compression scheme is the one introduced by Lempel and Ziv about 40 years ago. This compression scheme is known as "dictionary-based compression" and consists of squeezing an input string by replacing some of its substrings with (shorter) codewords which are actually pointers to a dictionary of phrases built as the string is processed. Surprisingly enough, although many fundamental results are nowadays known about upper bounds on the speed and effectiveness of this compression process and references therein), ``we are not aware of any parsing scheme that achieves optimality when the LZ77-dictionary is in use under any constraint on the codewords other than being of equal length'' [N. Rajpoot and C. Sahinalp. Handbook of Lossless Data Compression, chapter Dictionary-based data compression. Academic Press, 2002. pag. 159]. Here optimality means to achieve the minimum number of bits in compressing each individual input string, without any assumption on its generating source. In this paper we provide the first LZ-based compressor which computes the bit-optimal parsing of any input string in efficient time and optimal space, for a general class of variable-length codeword encodings which encompasses most of the ones typically used in data compression and in the design of search engines and compressed indexes.
We present the first thorough practical study of the Lempel-Ziv-78 and the Lempel-Ziv-Welch computation based on trie data structures. With a careful selection of trie representations we can beat well-tuned popular trie data structures like Judy, m-Bonsai or Cedar.
Simple and fast decoding is one of the main advantages of LZ77-type text encoding used in many popular file compressors such as gzip and 7zip. With the recent introduction of external memory algorithms for Lempel-Ziv factorization there is a need for external memory LZ77 decoding but the standard algorithm makes random accesses to the text and cannot be trivially modified for external memory computation. We describe the first external memory algorithms for LZ77 decoding, prove that their I/O complexity is optimal, and demonstrate that they are very fast in practice, only about three times slow…
In this paper we compare the difference in performance of two of the Prediction by Partial Matching (PPM) family of compressors (PPM$^*$ and the original Bounded PPM algorithm) and the Lempel-Ziv 78 (LZ) algorithm. We construct an infinite binary sequence whose worst-case compression ratio for PPM$^*$ is $0$, while Bounded PPM's and LZ's best-case compression ratios are at least $1/2$ and $1$ respectively. This sequence is an enumeration of all binary strings in order of length, i.e. all strings of length $1$ followed by all strings of length $2$ and so on. It is therefore normal, and is built…
We present an algorithm which computes the Lempel-Ziv factorization of a word $W$ of length $n$ on an alphabet $Σ$ of size $σ$ online in the following sense: it reads $W$ starting from the left, and, after reading each $r = O(\log_σ n)$ characters of $W$, updates the Lempel-Ziv factorization. The algorithm requires $O(n \log σ)$ bits of space and O(n \log^2 n) time. The basis of the algorithm is a sparse suffix tree combined with wavelet trees.
For both the Lempel Ziv 77- and 78-factorization we propose algorithms generating the respective factorization using $(1+ε) n \lg n + O(n)$ bits (for any positive constant $ε\le 1$) working space (including the space for the output) for any text of size \$n\$ over an integer alphabet in $O(n / ε^{2})$ time.
We propose a new representation of the offsets of the Lempel-Ziv (LZ) factorization based on the co-lexicographic order of the processed prefixes. The selected offsets tend to approach the k-th order empirical entropy. Our evaluations show that this choice of offsets is superior to the rightmost LZ parsing and the bit-optimal LZ parsing on datasets with small high-order entropy.
We present an algorithm that computes the Lempel-Ziv decomposition in $O(n(\logσ+ \log\log n))$ time and $n\logσ+ εn$ bits of space, where $ε$ is a constant rational parameter, $n$ is the length of the input string, and $σ$ is the alphabet size. The $n\logσ$ bits in the space bound are for the input string itself which is treated as read-only.
The complexity of computing the Lempel-Ziv factorization and the set of all runs (= maximal repetitions) is studied in the decision tree model of computation over ordered alphabet. It is known that both these problems can be solved by RAM algorithms in $O(n\logσ)$ time, where $n$ is the length of the input string and $σ$ is the number of distinct letters in it. We prove an $Ω(n\logσ)$ lower bound on the number of comparisons required to construct the Lempel-Ziv factorization and thereby conclude that a popular technique of computation of runs using the Lempel-Ziv factorization cannot achieve a…
We present a new, simple, and efficient approach for computing the Lempel-Ziv (LZ77) factorization of a string in linear time, based on suffix arrays. Computational experiments on various data sets show that our approach constantly outperforms the currently fastest algorithm LZ OG (Ohlebusch and Gog 2011), and can be up to 2 to 3 times faster in the processing after obtaining the suffix array, while requiring the same or a little more space.
We study the approximate string matching and regular expression matching problem for the case when the text to be searched is compressed with the Ziv-Lempel adaptive dictionary compression schemes. We present a time-space trade-off that leads to algorithms improving the previously known complexities for both problems. In particular, we significantly improve the space bounds, which in practical applications are likely to be a bottleneck.
We show that both the Lempel Ziv 77- and the 78-factorization of a text of length $n$ on an integer alphabet of size $σ$ can be computed in $O(n \lg \lg σ)$ time (linear time if we allow randomization) using $O(n \lg σ)$ bits of working space. Given that a compressed representation of the suffix tree is loaded into RAM, we can compute both factorizations in $O(n)$ time using $z \lg n + O(n)$ bits of space, where $z$ is the number of factors.
Lyndon factorization and Lempel-Ziv (LZ) factorization are both important tools for analysing the structure and complexity of strings, but their combinatorial structure is very different. In this paper, we establish the first direct connection between the two by showing that while the Lyndon factorization can be bigger than the non-overlapping LZ factorization (which we demonstrate by describing a new, non-trivial family of strings) it is never more than twice the size.
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 algori…
Countless variants of the Lempel-Ziv compression are widely used in many real-life applications. This paper is concerned with a natural modification of the classical pattern matching problem inspired by the popularity of such compression methods: given an uncompressed pattern s[1..m] and a Lempel-Ziv representation of a string t[1..N], does s occur in t? Farach and Thorup gave a randomized O(nlog^2(N/n)+m) time solution for this problem, where n is the size of the compressed representation of t. We improve their result by developing a faster and fully deterministic O(nlog(N/n)+m) time algorith…
We use the traveling front approach to derive exact asymptotic results for the statistics of the number of particles in a class of directed diffusion limited aggregation models on a Cayley tree. We point out that some aspects of these models are closely connected to two different problems in computer science, namely the digital search tree problem in data structures and the Lempel-Ziv algorithm for data compression. The statistics of the number of particles studied here is related to the statistics of height in digital search trees which, in turn, is related to the statistics of the length of …
We introduce a new approach to LZ77 factorization that uses O(n/d) words of working space and O(dn) time for any d >= 1 (for polylogarithmic alphabet sizes). We also describe carefully engineered implementations of alternative approaches to lightweight LZ77 factorization. Extensive experiments show that the new algorithm is superior in most cases, particularly at the lowest memory levels and for highly repetitive data. As a part of the algorithm, we describe new methods for computing matching statistics which may be of independent interest.
The well-known dictionary-based algorithms of the Lempel-Ziv (LZ) 77 family are the basis of several universal lossless compression techniques. These algorithms are asymmetric regarding encoding/decoding time and memory requirements, with the former being much more demanding. In the past years, considerable attention has been devoted to the problem of finding efficient data structures to support these searches, aiming at optimizing the encoders in terms of speed and memory. Hash tables, binary search trees and suffix trees have been widely used for this purpose, as they allow fast search at th…
Computing the LZ factorization (or LZ77 parsing) of a string is a computational bottleneck in many diverse applications, including data compression, text indexing, and pattern discovery. We describe new linear time LZ factorization algorithms, some of which require only 2n log n + O(log n) bits of working space to factorize a string of length n. These are the most space efficient linear time algorithms to date, using n log n bits less space than any previous linear time algorithm. The algorithms are also practical, simple to implement, and very fast in practice.
The Lempel-Ziv parsing of a string (LZ77 for short) is one of the most important and widely-used algorithmic tools in data compression and string processing. We show that the Lempel-Ziv parsing of a string of length $n$ on an alphabet of size $σ$ can be computed in $O(n\log\logσ)$ time ($O(n)$ time if we allow randomization) using $O(n\logσ)$ bits of working space; that is, using space proportional to that of the input string in bits. The previous fastest algorithm using $O(n\logσ)$ space takes $O(n(\logσ+\log\log n))$ time. We also consider the important rightmost variant of the problem, wher…
For decades, computing the LZ factorization (or LZ77 parsing) of a string has been a requisite and computationally intensive step in many diverse applications, including text indexing and data compression. Many algorithms for LZ77 parsing have been discovered over the years; however, despite the increasing need to apply LZ77 to massive data sets, no algorithm to date scales to inputs that exceed the size of internal memory. In this paper we describe the first algorithm for computing the LZ77 parsing in external memory. Our algorithm is fast in practice and will allow the next generation of tex…
Relative Lempel-Ziv (RLZ) is a popular algorithm for compressing databases of genomes from individuals of the same species when fast random access is desired. With Kuruppu et al.'s (SPIRE 2010) original implementation, a reference genome is selected and then the other genomes are greedily parsed into phrases exactly matching substrings of the reference. Deorowicz and Grabowski (Bioinformatics, 2011) pointed out that letting each phrase end with a mismatch character usually gives better compression because many of the differences between individuals' genomes are single-nucleotide substitutions.…
We present a new on-line algorithm for computing the Lempel-Ziv factorization of a string that runs in $O(N\log N)$ time and uses only $O(N\logσ)$ bits of working space, where $N$ is the length of the string and $σ$ is the size of the alphabet. This is a notable improvement compared to the performance of previous on-line algorithms using the same order of working space but running in either $O(N\log^3N)$ time (Okanohara & Sadakane 2009) or $O(N\log^2N)$ time (Starikovskaya 2012). The key to our new algorithm is in the utilization of an elegant but less popular index structure called Directed A…
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 th…
We consider the problem of decompressing the Lempel--Ziv 77 representation of a string $S$ of length $n$ using a working space as close as possible to the size $z$ of the input. The folklore solution for the problem runs in $O(n)$ time but requires random access to the whole decompressed text. Another folklore solution is to convert LZ77 into a grammar of size $O(z\log(n/z))$ and then stream $S$ in linear time. In this paper, we show that $O(n)$ time and $O(z)$ working space can be achieved for constant-size alphabets. On general alphabets of size $σ$, we describe (i) a trade-off achieving $O(…
We present a simple adaptation of the Lempel Ziv 78' (LZ78) compression scheme ({\em IEEE Transactions on Information Theory, 1978}) that supports efficient random access to the input string. Namely, given query access to the compressed string, it is possible to efficiently recover any symbol of the input string. The compression algorithm is given as input a parameter $\eps >0$, and with very high probability increases the length of the compressed string by at most a factor of $(1+\eps)$. The access time is $O(\log n + 1/\eps^2)$ in expectation, and $O(\log n/\eps^2)$ with high probability. Th…
Much research has been devoted to optimizing algorithms of the Lempel-Ziv (LZ) 77 family, both in terms of speed and memory requirements. Binary search trees and suffix trees (ST) are data structures that have been often used for this purpose, as they allow fast searches at the expense of memory usage. In recent years, there has been interest on suffix arrays (SA), due to their simplicity and low memory requirements. One key issue is that an SA can solve the sub-string problem almost as efficiently as an ST, using less memory. This paper proposes two new SA-based algorithms for LZ encoding, wh…
Lempel-Ziv (LZ77 or, briefly, LZ) is one of the most effective and widely-used compressors for repetitive texts. However, the existing efficient methods computing the exact LZ parsing have to use linear or close to linear space to index the input text during the construction of the parsing, which is prohibitive for long inputs. An alternative is Relative Lempel-Ziv (RLZ), which indexes only a fixed reference sequence, whose size can be controlled. Deriving the reference sequence by sampling the text yields reasonable compression ratios for RLZ, but performance is not always competitive with th…
The so-called "one-bit catastrophe" for the compression algorithm LZ'78 asks whether the compression ratio of an infinite word can change when a single bit is added in front of it. We answer positively this open question raised by Lutz and others: we show that there exists an infinite word $w$ such that $ρ_{sup}(w)=0$ but $ρ_{inf}(0w)>0$, where $ρ_{sup}$ and $ρ_{inf}$ are respectively the $\limsup$ and the $\liminf$ of the compression ratios $ρ$ of the prefixes. To that purpose we explore the behaviour of LZ'78 on finite words and show the following results: - There is a constant $C>0$ such th…
Compression techniques that support fast random access are a core component of any information system. Current state-of-the-art methods group documents into fixed-sized blocks and compress each block with a general-purpose adaptive algorithm such as GZIP. Random access to a specific document then requires decompression of a block. The choice of block size is critical: it trades between compression effectiveness and document retrieval times. In this paper we present a scalable compression method for large document collections that allows fast random access. We build a representative sample of t…
Relative Lempel-Ziv (RLZ) parsing is a dictionary compression method in which a string $S$ is compressed relative to a second string $R$ (called the reference) by parsing $S$ into a sequence of substrings that occur in $R$. RLZ is particularly effective at compressing sets of strings that have a high degree of similarity to the reference string, such as a set of genomes of individuals from the same species. With the now cheap cost of DNA sequencing, such data sets have become extremely abundant and are rapidly growing. In this paper, instead of using a single reference string for the entire co…
Given a string $S$, the \emph{compressed indexing problem} is to preprocess $S$ into a compressed representation that supports fast \emph{substring queries}. The goal is to use little space relative to the compressed size of $S$ while supporting fast queries. We present a compressed index based on the Lempel--Ziv 1977 compression scheme. We obtain the following time-space trade-offs: For constant-sized alphabets; (i) $O(m + occ \lg\lg n)$ time using $O(z\lg(n/z)\lg\lg z)$ space, or (ii) $O(m(1 + \frac{\lg^εz}{\lg(n/z)}) + occ(\lg\lg n + \lg^εz))$ time using $O(z\lg(n/z))$ space. For integer al…
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…
The Block Tree is a recently proposed data structure that reaches compression close to Lempel-Ziv while supporting efficient direct access to text substrings. In this paper we show how a self-index can be built on top of a Block Tree so that it provides efficient pattern searches while using space proportional to that of the original data structure. More precisely, if a Lempel-Ziv parse cuts a text of length $n$ into $z$ non-overlapping phrases, then our index uses $O(z\log(n/z))$ words and finds the $occ$ occurrences of a pattern of length $m$ in time $O(m\log n+occ\log^εn)$ for any constant …
Lempel-Ziv is an easy-to-compute member of a wide family of so-called macro schemes; it restricts pointers to go in one direction only. Optimal bidirectional macro schemes are NP-complete to find, but they may provide much better compression on highly repetitive sequences. We consider the problem of approximating optimal bidirectional macro schemes. We describe a simulated annealing algorithm that usually converges quickly. Moreover, in some cases, we obtain bidirectional macro schemes that are provably a 2-approximation of the optimal. We test our algorithm on a number of artificial repetitiv…
We raise the question of approximating the compressibility of a string with respect to a fixed compression scheme, in sublinear time. We study this question in detail for two popular lossless compression schemes: run-length encoding (RLE) and Lempel-Ziv (LZ), and present sublinear algorithms for approximating compressibility with respect to both schemes. We also give several lower bounds that show that our algorithms for both schemes cannot be improved significantly. Our investigation of LZ yields results whose interest goes beyond the initial questions we set out to study. In particular, we p…
In this paper we give an infinite family of strings for which the length of the Lempel-Ziv'77 parse is a factor $Ω(\log n/\log\log n)$ smaller than the smallest run-length grammar.
We show how to compress string dictionaries using the Lempel-Ziv (LZ78) data compression algorithm. Our approach is validated experimentally on dictionaries of up to 1.5 GB of uncompressed text. We achieve compression ratios often outperforming the existing alternatives, especially on dictionaries containing many repeated substrings. Our query times remain competitive.
Tries are among the most versatile and widely used data structures on words. They are pertinent to the (internal) structure of (stored) words and several splitting procedures used in diverse contexts ranging from document taxonomy to IP addresses lookup, from data compression (i.e., Lempel-Ziv'77 scheme) to dynamic hashing, from partial-match queries to speech recognition, from leader election algorithms to distributed hashing tables and graph compression. While the performance of tries under a realistic probabilistic model is of significant importance, its analysis, even for simplest memoryle…
We describe a data structure that stores a string $S$ in space similar to that of its Lempel-Ziv encoding and efficiently supports access, rank and select queries. These queries are fundamental for implementing succinct and compressed data structures, such as compressed trees and graphs. We show that our data structure can be built in a scalable manner and is both small and fast in practice compared to other data structures supporting such queries.
We present RCT, a new compact data structure to represent trajectories of objects. It is based on a relative compression technique called Relative Lempel-Ziv (RLZ), which compresses sequences by applying an LZ77 encoding with respect to an artificial reference. Combined with $O(z)$-sized data structures on the sequence of phrases that allows to solve trajectory and spatio-temporal queries efficiently. We plan that RCT improves in compression and time performance the previous compressed representations in the state of the art.
Adaptive codes have been introduced in [Dragos Trinca, cs.DS/0505007] as a new class of non-standard variable-length codes. These codes associate variable-length codewords to symbols being encoded depending on the previous symbols in the input data string. A new data compression algorithm, called EAH, has been introduced in [Dragos Trinca, cs.DS/0505061], where we have behaviorally shown that for a large class of input data strings, this algorithm substantially outperforms the well-known Lempel-Ziv universal data compression algorithm. In this paper, we translate the EAH encoder into automata …
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 ac…
Adaptive codes associate variable-length codewords to symbols being encoded depending on the previous symbols in the input data string. This class of codes has been introduced in [Dragos Trinca, cs.DS/0505007] as a new class of non-standard variable-length codes. New algorithms for data compression, based on adaptive codes of order one, have been presented in [Dragos Trinca, ITCC-2004], where we have behaviorally shown that for a large class of input data strings, these algorithms substantially outperform the Lempel-Ziv universal data compression algorithm. EAH has been introduced in [Dragos T…
The sensitivity of a string compression algorithm $C$ asks how much the output size $C(T)$ for an input string $T$ can increase when a single character edit operation is performed on $T$. This notion enables one to measure the robustness of compression algorithms in terms of errors and/or dynamic changes occurring in the input string. In this paper, we analyze the worst-case multiplicative sensitivity of string compression algorithms, which is defined by $\max_{T \in Σ^n}\{C(T')/C(T) : ed(T, T') = 1\}$, where $ed(T, T')$ denotes the edit distance between $T$ and $T'$. For the most common versi…
Dynamic dictionary-based compression schemes are the most daily used data compression schemes since they appeared in the foundational papers of Ziv and Lempel in 1977, commonly referred to as LZ77. Their work is the base of Deflate, gZip, WinZip, 7Zip and many others compression software. All of those compression schemes use variants of the greedy approach to parse the text into dictionary phrases. Greedy parsing optimality was proved by Cohn et al. (1996) for fixed length code and unbounded dictionaries. The optimality of the greedy parsing was never proved for bounded size dictionary which a…
Converting a compressed format of a string into another compressed format without an explicit decompression is one of the central research topics in string processing. We discuss the problem of converting the run-length Burrows-Wheeler Transform (RLBWT) of a string to Lempel-Ziv 77 (LZ77) phrases of the reversed string. The first results with Policriti and Prezza's conversion algorithm [Algorithmica 2018] were $O(n \log r)$ time and $O(r)$ working space for length of the string $n$, number of runs $r$ in the RLBWT, and number of LZ77 phrases $z$. Recent results with Kempa's conversion algorith…
