array of integers that stores the lcp value between consecutive suffixes in SA, such that LCP[ ] = lcp( SA[ −1] , SA[ ] ), for 1 < ≤ , and we define
[1] = 0. The suffixes starting with the same symbol ∈ Σ form a -bucket in SA. The head and the tail of a -bucket refer to the first and the last position of the -bucket in SA.
Let = (Σ, Γ, , ) be a reduced context-free grammar (i.e, with no unreachable non-terminals) such that Σ is the terminal alphabet of ; Γ is the set of non-terminal symbols (disjoint from Σ); ⊆ Γ × (Σ ∪ Γ) * is the set of production rules; and ∈ Γ is the start symbol.
A production rule ( , ) is also denoted → . In this case, it is said that is derived from . For strings , ∈ (Σ ∪ Γ) * , if is obtained from by production rules in , then is derived from . When is obtained by a (possibly empty) sequence of derivations from , then is generated from .
Given a string , the grammar compression problem is to find a grammar that generates only , such that can be represented in less space than the original . Given that grammar-compresses , for ( , ) ∈ , G( ) = is defined as the single string ∈ Σ * that is generated from . The language generated by contains the unique string G( ) = . This notion can be extended further for a string of terminals and non-terminals , such that:
When each [ ], 1 ≤ ≤ | |, generates a single sequence, the previous definition can be replaced by the concatenation of the strings generated by [ ], 1 ≤ ≤ | |:
We cover various techniques to encode sequences of integers, when most of them are expected to be small. Some of the techniques allow us to directly access any integer in the sequence.
Simple8b. The Simple8b scheme, proposed by Anh and Moffat [2010] , encodes a sequence of small integers in a 64-bit word using the number of bits required by the largest integer. Basically, it identifies a word with a 4-bit tag called selector, which specifies the number of integers encoded in the rest of the word and the width of such integers.
Simple8b also has specific selectors for a run consisting of zeroes. If a run of 240 or 120 zeros is encountered, it can be represented with a single 64-bit word.
Directly Addressable Codes. The Directly Addressable Codes (DAC) proposed by Brisaboa et al. [2013] Then a layered data structure is constructed in such a way that the -th layer contains two bitmaps: the first bitmap is the concatenation of blocks , , for every 1 ≤ ≤ , whereas the second bitmap is the concatenation of the bits , associated with each block , .
To retrieve any given value [ ], one must first recover ,1 and check if its value is zero. If so [ ] equals ,1 , otherwise, it is necessary to proceed recursively to the -th entry of the next layer, where = =1 ,1 , and append the result of the recursive call to ,1 . The prefix sum value can be computed in constant time by using auxiliary data structures on top of the bitmaps.
Elias-Fano Encoding. This format permits the encoding of a monotonically increasing sequence of integers over the interval [0, − 1] within 2 + ⌈lg ⌉ bits and allows the retrieval of any integer of such sequence in constant time [Vigna 2013 ]. Each integer is divided into two parts: , the ⌈lg ⌉ most significant bits of and , the ⌈lg ⌉ remaining bits of . The values are concatenated in a single array of ⌈lg ⌉ bits and each value is classified in one of the total of possible buckets. Then, the number of elements of each bucket is represented in a negated unary representation and such representations are concatenated in a bitmap of 2 bits, bits for each possible bucket and further bits for every element .
To retrieve the -th value of the sequence of integers, one just needs to search for the position of the -th one bit on , and append to the binary representation of − . The position can be retrieved in constant time using auxiliary data structures on top of .
SAIS [Nong et al. 2009 ] builds on the induced suffix sorting technique introduced by previous algorithms [Itoh and Tanaka 1999; Ko and Aluru 2003] . Induced suffix sorting consists in deducing the order of unsorted suffixes from a (smaller) set of already ordered suffixes.
The next definition classifies suffixes and symbols of strings.
Definition 1 (L-type and S-type). For any string , = $ has type S. A suffix is an S-suffix if < +1 , otherwise is an L-suffix. Each symbol [ ] has the type of .
The suffixes can be classified in linear time by scanning once from right to left, so that the type of each suffix is stored in a bitmap of size .
Note that, within a -bucket, the L-suffixes precede the S-suffixes.
Further, the classification of suffixes is refined as follows:
Definition 2 (LMS-type). Let be a string. Then is an LMS-suffix if is an S-suffix and −1 is an L-suffix. Nong et al. [2009] showed that the order of the LMS-suffixes is enough to induce the order of all suffixes. This is the basis of the SAIS algorithm.
(1) Sort the LMS-suffixes. This step is explained later.
(2) Insert the LMS-suffixes into the tail of their respective -buckets in SA[1, ], without changing their order. Now SA contains the LMS-suffixes positions, in sorted order, on the end of each -bucket. The remaining values of SA are initialized with a sentinel ⊥ value.
( Whenever a value is inserted in the head (or tail) of a -bucket, the pointer to the head (or tail) is increased (or decreased) by one.
In order to sort the LMS-suffixes in Step 1, [1, ] is divided (factorized) into LMS-substrings.
Definition 3.
[ , ] is an LMS-substring if both and are LMS-suffixes, but no suffix between and has LMS-type.
The last suffix is an LMS-substring.
Let 1 1 , 1 2 , . . . , 1 1 be the 1 LMS-substrings of read from left to right. A modified version of SAIS is applied to sort the LMS-substrings. Starting from Step 2, [1, ] is scanned (right-to-left) and each new LMS-suffix starting with is inserted (bucket-sorted) at the tail of its -bucket. Steps 3 and 4 work exactly the same. At the end, the LMS-substrings are sorted and the beginning positions of each LMS-substring are stored in their corresponding -buckets in SA.
A name 1 is assigned to each LMS-substring 1 according to its lexicographical rank in [1, 1 ], such that 1 ≤ 1 iff 1 ≤ 1 , where 1 is the number of different LMS-substrings in . In order to compute the names, each consecutive LMS-substrings in SA, say 1 and 1 +1 , are compared to determine if either 1 = 1 +1 or 1 < 1 +1 . In the former case 1 +1 is set to 1 , whereas in the latter case 1 +1 is set to 1 + 1. This procedure may be sped up by comparing the LMS-substrings first by symbol and then by type, with L-type symbols being smaller than S-type symbols in case of ties [Nong et al. 2011 ].
A new (reduced) string 1 = 1 1 · 1 1 · · · 1 1 is created, whose length 1 is at most /2, and the alphabet size 1 is integer. If every 1 ≠ 1 then all LMS-suffixes are already sorted. Otherwise, SAIS is recursively applied to sort all the suffixes of 1 . Nong et al. [Nong et al. 2009 ] showed that the relative order of the LMS-suffixes in and the order of the respective suffixes in 1 are the same. Therefore, the order of all LMS-suffixes can be determined by the result of the recursive algorithm.
This section introduces the Grammar Compression algorithm by Induced Sorting (GCIS).
First, a context-free grammar = (Σ, Γ, , ) that generates only [1, ] is computed. To do this SAIS is modified as follows.
Consider the -th recursion level. In Step 1, after the input string [1, ] is divided into the LMS-substrings 1 , 2 , . . . , +1 and named 1 , 2 , . . . , +1 , a new rule → is created for each different LMS-substring . Moreover, an additional rule 0 → −1 0
with 1 standing for the index of the leftmost LMS-type suffix of , is created for the prefix of that is not included in any LMS-substring. In this context, when = 0, 0 = and 0 = .
The algorithm is then called recursively with the reduced string +1 = 1 · 2 · · · +1 as input as long as +1 < +1 , that is, the LMS-substrings are not pairwise distinct. At the end, when ℓ = ℓ , the last recursion level = ℓ is reached, and the start symbol of of is created so that the initial production → ℓ 0 · ℓ 1 · ℓ 2 · · · ℓ ℓ generates the original string [1, ].
The algorithm stops after computing , since we are not interested in constructing the suffix array; Steps 2, 3 and 4 of SAIS are not executed. The recursive calls return to the top level and a grammar that generates only [1, ] has been computed.
Since for each LMS-substring there is a unique , there are no cycles in any generation. Further, there is only one path of derivations that from a string generates a string ′ . The consequence of this deterministic choice, for every derivation, is that G( ), for ∈ Γ, is a fixed string of terminals. Figure 1 shows the grammar construction on GCIS. Fig. 1 . Grammar construction during GCIS. All LMS-substrings (those starting with a ' * ' symbol), are sorted according to SAIS framework, and then rules 0 0 → AG, 0 1 → $, . . . , 0 7 → are created. Next, 1 is obtained by replacing every LMS-substring by the left-hand side of its rule. The procedure is applied recursively to 1 . When 2 is created, the alphabet size is equal to | 2 | = 2 , and thus the starting rule that generates 0 is obtained.
Grammar compression. Consecutive entries in the set of productions are likely to share a common prefix, since the LMS-substrings are given lexicographically ordered by SAIS. Therefore, each rule → ∈ is encoded using two values ( , s( )), such that encodes lcp ( −1 , ), and the remaining symbols of are given by s( ) = [ + 1, | |].
For each starting rule 0 , we define = 0. This technique is known as Front-coding [Witten et al. 1999 ].
The computation of ( , s( )) is performed with no additional cost with a slight modification in the naming procedure of SAIS. Each consecutive LMS-substring in SA, say −1 and , are compared first by symbol until a mismatch is found, and then compared by type, to check if either −1 = or −1 < . The symbol-wise comparison reveals lcp( −1 , ) as well, so the resulting complexity is the same with a small slowdown in the running time.
Time complexity. GCIS runs in ( ) time, since each step of the modified SAIS runs in linear time and the length of the reduced string is at most | −1 |/2. Implementation details. Each non-terminal is represented by a pair = ( , s( )), as explained. The values tend to be small and, considering the -th recursion value, the sum of such values cannot be greater than , since no two LMS-substrings overlap by more than one symbol.
One can encode all values by using the Simple8b encoding in an integer array . All strings s( ) are encoded in a single fixed-width integer array , of cell width ⌊lg( )⌋ + 1 bits. The length of each s( ) is also encoded using
Simple8b into a word array . The same observation of the lcp sum can be done here: the sum of all |s( )| on the
The decoding process is done level-wise, starting from the last recursion level = ℓ, by decoding the right side of each rule. At the end, is decoded from 1 .
In the -th recursion level, the values ( , , ) from , and , the data structures mentioned in the implementation details of Section 4.1, are decoded sequentially. In order to obtain the right-hand side of the production rules +1 from , the first symbols of are copied to +1 and the symbols from , which correspond to the string , are appended to +1 . After this process the plain representation of each rule is stored, in a single array of cells with fixed width ⌊lg( )⌋ + 1 bits. An additional array of pointers is also created to find the starting position of each rule in this fixed-width array.
With the fixed-width array and the array of pointers , −1 now can be decoded from . First, the right side of 0 is copied into −1 . Then, is scanned in a left-to-right fashion and for each [ ] the algorithm appends to −1 the right-hand side of the non-terminal [ ], which can be easily found with support of array in constant time.
Time complexity. The whole decompression process takes ( ) time.
In order to support extraction of substrings from the compressed text, it is necessary to augment the dictionary with two additional data structures: , a partial-sum on the lengths of the symbols in the reduced string ℓ of the last recursion level, and , a data structure that for each non-terminal stores |G( )|. Formally, those data structures are defined as:
The data structure can also be defined recursively as:
To obtain a substring [ , ], we then proceed as follows:
(1) With a binary search, locate indices and from such that:
(2) Let ℓ be the number of levels in GCIS grammar and the string derived from . Then define ℓ = [ , ] and follow the next steps for = ℓ to = 1.
(3) Apply a derivation step to each non-terminal ∈ to obtain a new string −1 . Note that G(
(4) Trim −1 from the left and right as much as possible as long as it generates a superstring of [ , ] . This can be done efficiently because we know the lenght of G( ), for every ∈ Σ ∪ Γ. (b) If > 1, then −1 contains only non-terminal symbols and generates a superstring
. We then trim −1 by using and finding, with a linear search, two indices and of −1 such that:
before proceeding. Figure 2 shows an example for extracting a text using the aforementioned procedure. Figure 1 . Initially, a binary search is performed on to identify the substring of 2 that shall be decompressed: 2 [2, 4], which generates [9, 43], is decompressed to obtain 1 . A linear scan is performed in both ends considering the length of the terminals generated by each rule of 1 to find the indexes = 2 and = 8. 1 [2, 8] is then decompressed and 0 = [9, 36] is obtained, which makes possible to extract [10, 34] by simply ignoring both ends. Implementation details. Since the length of the string ℓ is much shorter than the original text in practice, the verbatim representation of as an array of integers is affordable.
The array is represented using DACs. This representation allows efficient access while representing the data in a compact way.
To support fast extraction, we need to efficiently decompress a single rule. Simple8b encoding works very well when the objective is compressing or decompressing since all the rules are first expanded sequentially in the decompressing stage. However, when the aim is to extract symbols, we need to expand individual rules. Thus, instead of encoding all the lcp values with the simple8b scheme, Elias-Fano encoding is employed, allowing us to retrieve a random lcp value of a rule efficiently and hence the decoding of a random rule. The length of each s( ) is also encoded using Elias-Fano and the s( ) values are encoded in a fixed-width integer array. Since the lcp values are front-encoded, we force that every -th lcp value is set to 0, with ∈ (1). This setting does not have a significant impact on compression and ensures that we have to backtrack a constant number of rules to extract an individual rule prefix.
The suffix array (SA) construction boils down to sorting all suffixes of . Although GCIS compression does not sort suffixes, it executes Step 1 of SAIS and the production rules created correspond to the LMS-substrings already sorted, which is used by SAIS for sorting all suffixes. We show next how to modify our decompression algorithm for building SA as a byproduct with, asymptotically, no additional overhead. First, when = ℓ, → ℓ is taken, and its suffix array SA is built directly as SA [ [ ]] = . Nong et al. [2009] observed that SA also gives the order of all LMS-suffixes of string −1 . Then, −1 is decoded (Section 4.2), and Steps 2, 3 and 4 of SAIS (Section 3) are executed to obtain SA −1 , and so on. The algorithm proceeds for = ℓ − 1, . . . , 1, obtaining the reduced string −1 together with SA −1 at each iteration. At the end, the original string is decoded from 1 and SA is induced from SA 1 .
Time Complexity. SA is built in ( ) time, since each step of SAIS is linear and the length of all reduced strings is ( ).
When |Σ| ∈ (1), the longest common prefix (LCP) array can also be computed in linear time within the induced suffix sorting framework [Fischer 2011; Louza et al. 2017b ]. We show below how to modify our decompression algorithm to compute SA and LCP together with, asymptotically, no additional cost. When = 1, the original string is decoded from 1 , and SA 1 stores the order of all LMS-suffixes of . Then, in linear time, we compute the LCP array of the LMS-suffixes using a sparse variant of Φ-algorithm by Kärkkäinen et al. [2009] , which avoids storing auxiliary arrays by reusing the space of SA[ /2, ] and LCP[ /2, ]. The lcp-values between the LMS-suffixes are used to induce the lcp-values between the L-suffixes during Step 3 (Section 3), and these are used to induce the S-suffixes during Step 4 (see Louza et al. [2017b] for details). Given an additional stack of ( log ) bits , each lcp-value induction is done in ( ) time, which we assume to be constant at the top recursion level.
Therefore, at the end we have computed SA and LCP as a byproduct of GCIS decompression. 
To confirm the practical value of GCIS, we conducted experiments using several corpora. We measured compression and decompression speed, compression ratio, and memory usage during compression and decompression of GCIS against classical and grammar-based compressors (Section 5.4), evaluated the extraction of symbols (Section 5.6) and
showed the efficiency of suffix array construction of GCIS during the decompression (Section 5.7).
In the following subsections we describe and discuss the experimental setup and the results.
Regular texts were taken from the corpora large-corpus [Trigell 1998 ], enwiki [Mahoney 2006 ], manzini [Manzini 2003 ], pizza-chili [Ferragina and Navarro 2005a] and silesia [Deorowicz 2003 ]. Repetitive texts were chosen from pizza-chili-repetitive corpus [Ferragina and Navarro 2005b] Tables 1, 2 and 3 summarize the chosen texts and their size, grouping in boxes texts from the same corpus. 
To evaluate GCIS in compression speed, decompression speed and compression ratio, we chose the well-known compressors Gzip [Gailly and Adler 2017] , Bzip2 [Seward 1996] , 7-zip [Pavlov 2017 ]; the statistical compressor Ppmdj [Shkarin 2006 ]; the grammar compressor RePair [Wan 2014 ], the Lempel-Ziv approximation for very large texts ReLZ [Kosolobov et al. 2020] , and the RePair approximation for very large texts BigRePair [Gagie et al. 2019 ].
Regarding extraction of symbols, we compared GCIS with different encodings of RePair grammars that allow fast extraction. These encodings can be represented in a more straight-forward way, storing G( ), for ∈ Σ ∪ Γ, or in a more elaborated way, creating succinct tree data structures that replace the original grammar encoding while allowing one obtain the right-hand side of any rule, as described by Maruyama and Tabei [2013] . The implementation of such data structures was based on the work of Gagie et al. [2020] and can be found in [I 2020] . We used the following encodings:
• PlainSlp_32Fblc: uses 32-bit integers for the array representations.
• PlainSlp_FblcFblc: employs the minimum bit length required to represent the maximum value of a given integer array. • PlainSlp_IblcFblc: uses roughly ⌈lg ⌉ bits to represent the -th rule exploiting that the -th rule is less than .
For representing G( ), for ∈ Σ ∪ Γ, it uses the same strategy of PlainSlp_FblcFblc.
• PoSlp_Iblc: employs the approach POSLP of Maruyama and Tabei [2013] to represent the parse tree and encodes the leaves using roughly ⌈lg ⌉ bits for the -th rule.
• PoSlp_Sd: applies the POSLP approach of Maruyama and Tabei [2013] to represent the parse tree and encodes the leaves with Elias-Fano.
In order to assess the computation of suffix and LCP arrays directly from decompression, GCIS was compared with efficient suffix and LCP construction algorithms implemented by sais-lite [Kurpicz 2015; Mori 2010] and divsufsort [Kurpicz 2016; Mori 2008 ].
GCIS source code and a detailed description of the processed data are available at https://github.com/danielsaad/gcis.
Due to memory capacity and availability, we conducted the experiments in two machines, one for the regular and repetitive corpora and another for the very large datasets. Their specifications follow:
Machine #1, used for regular and repetitive texts:
• CPU: 2x Intel(R) Xeon(R) CPU E5-2640 v3 @ 2.60GHz CPUs;
• RAM Memory: 64GB;
• Operating System: Centos7, kernel version 3.10.
Machine #2, used for very large datasets:
• CPU: 2x Intel(R) Xeon(R) E5-2630 v3 @ 2.40GHz;
• RAM Memory: 386GB;
• Operating System: Debian GNU/Linux 8, kernel version 3.16.
We compiled GCIS, RePair (and its extractors), BigRePair, Ppmdj and ReLZ under gcc with -O3 -NDEBUG flags. The default command line parameters of Gzip, Bzip2, Ppmdj, ReLZ were used on the experiments. A dictionary size of 1 GB was used in 7-zip. BigRePair RAM usage was limited to 10 GB.
GCIS was implemented in C++11 using the Succinct Data Structure Library (SDSL) version 2.0 [Gog et al. 2014 ].
We evaluated all compressors in terms of compression ratio, compression and decompression speed. We also considered their peak memory usage during compression and decompression. BigRePair could not compress some texts, so its corresponding data in the graphs are missing. Decompression in ReLZ is not implemented, nonetheless, ReLZ serves as a compression benchmark since it approximates the Lempel-Ziv parse.
It is important to remark that BigRePair does not produce a compact representation of rules, since it represents the right-hand side of its rules with 2 integers (all the rules are of length two). However, we optimized it by representing each rule with at most ⌈log 2 ⌉ bits, being the number of rules, and integrating the non-terminals that occur only once in their corresponding right-hand side. This saves ⌈log 2 ⌉ bits for each eliminated non-terminal.
For very large texts, only 7-zip, GCIS, ReLZ and BigRePair were evaluated, since they are the best choices for repetitive data. RePair was not considered because its data structures do not fit in main memory on such texts.
Compression ratio. It stands for the ratio between the compressed and the original text size, and it is given as percentage. Figure 3 shows that RePair outperforms GCIS and the approximations designed for very large texts, and it is competitive with a basic Lempel-Ziv compressor such as Gzip. However, it is clearly outperformed by Bzip2, Ppmdj and 7-zip. The latter displays the best compression ratio overall, being Ppmdj a close competitor in some cases. Figure 4 shows the compression ratios for the repetitive corpora. The compressors that exploit repetitiveness obtain much better compression ratios this time, whereas Gzip, Bzip2 and Ppmdj obtain similar compression ratios as on the regular texts. In particular, 7-zip obtains the best compression ratio in all cases, closely followed by RePair. The compression ratio of GCIS is about twice that of RePair in most cases, but it is still very good in absolute terms and outperforms ReLZ. BigRePair obtains a compression ratio between those of RePair and GCIS.
The results for the very large texts are depicted in Figure 5 . 7-zip is better for the text enwiki-20191120-20G. The situation stays as in the smaller repetitive files: BigRePair compresses more than GCIS, and GCIS compresses better than ReLZ, except for the the chromosome 19 based texts.
Compression speed. Figures 6,7 and 8 show the compression speed, in MB/s, of the compressors for each text type.
Gzip is the fastest compressor in most regular texts. GCIS is the second-fastest compressor, followed by Ppmdj. GCIS outperforms Bzip2 and ReLZ and is faster than the others by a wide margin. In particular, GCIS is typically an order of magnitude faster than the other grammar compressors (RePair and BigRePair), which are its direct competitors.
Considering repetitive texts, GCIS is still faster than ReLZ and 7-zip (and often faster than Bzip2); it is also orders of magnitude faster than RePair. Gzip is still generally the fastest, but its compression ratio is unacceptable for repetitive data. BigRePair is also slower than GCIS in most cases and, in the exceptions where BigRePair is faster, it outperforms GCIS by a small margin.
For very large texts, GCIS is much faster than 7-zip, which becomes the slowest of the considered compressors.
However, ReLZ and BigRePair become much faster than GCIS, as expected from being designed for this scenario. ReLZ is clearly the fastest compressor, though its compression ratio is the worst. A problem for GCIS on these very large files is that, once the text exceeds 2 GiB, it needs to use 64-bit integers, which doubles the memory requirements. BigRePair and ReLZ do not suffer from this problem and require a small amount of main memory during compression.
Decompression speed. Figure 9 depicts the results for regular texts. Gzip and RePair are the fastest at decompressing, followed by 7-zip and GCIS. Bzip2 and BigRePair are the slowest decompressors. Figure 10 shows that the situation is similar on repetitive texts, except that 7-zip becomes way faster than the others in almost all cases. Despite the relative differences, in absolute terms GCIS is still fast, decompressing the files in around 5 MB/s. The situation stays the same on the compressors that run on very large texts: as shown in Figure 11 , 7-zip is the fastest, followed by GCIS and then by BigRePair.
Peak memory. We evaluated the peak memory consumption (resident size) of all compressors during compression and decompression for each type of text; the results are shown in Figures 12 to 17 . Overall, Gzip, Bzip2 and Ppmdj require negligible space to compress or decompress. On regular texts, considering compression, GCIS is followed by ReLZ, 7-zip, BigRePair and RePair, the last being behind by a large margin. The situation is reversed in decompression: BigRePair is followed by RePair, 7-zip, and GCIS. On repetitive texts, the scenario is similar for compression, but GCIS escorts BigRePair. On very large texts, during compression ReLZ is the most space-efficient, followed by BigRePair, 7-zip and lastly by GCIS. Considering decompression the order stays the same, except for ReLZ, which does not decompress.
Figures 18 to 20 present conceptual radar charts that summarize, for each text type, the performance of all compressors in each rated aspect. The closer the values are to the pentagon borders, the better the compressor performed on the corresponding aspect.
Results depicted by Figures 21 and 22 show that GCIS is faster than the extractors on succinct encodings of RePair but slower than those running on the more straightfoward representation using integer arrays. In turn, regarding space on regular and repetitive texts, GCIS is more space-efficient than the straightfoward encodings but less space-efficient than the POSLP alternatives, as shown in Figures 23 and 24 . GCIS is then a competitive alternative regarding the space-time trade-off.
Considering the computation of SA and LCP arrays during decompression, we measured the total time to decompress the files with GCIS without generating a plain-text file, but instead inducing the SA and the LCP arrays. We compared these results with SAIS [Kurpicz 2015 ] and divsufsort [Fischer and Kurpicz 2017; Kurpicz 2016] implementations based on those of Yuta Mori, which are known as the fastest suffix array construction algorithms in practice. Figure 25 shows the SA and LCP construction on the 8 most repetitive real texts when only the GCIS compressed texts are available. Very large texts were not considered because the implementations of [Kurpicz 2015] and [Kurpicz 2016 ] only deal with 32-bit integers. GCIS builds the SA and LCP arrays faster than decompressing and then using the suffix array construction algorithms over the plain text.
The hatched part corresponds to the LCP computation and the black bar corresponds to the time spent in decompressing the text with GCIS to calculate SA and LCP values using the SAIS and divsufsort implementations.
We have introduced GCIS, a new grammar-based compression algorithm based on the induced suffix sorting framework of SAIS [Nong et al. 2009 ]. GCIS uses the meta-symbols introduced by SAIS to generate non-terminals of a balanced grammar that reproduces the original text. Our experiments on repetitive texts show that GCIS compresses 3-7 times faster than RePair and 7-zip. Compared to RePair, the grammar compressor that compresses the most, GCIS compresses using 3-5 times less memory, yet it obtains a compressed file twice as large (yet the absolute compression is still attractive, below 5% in most cases). GCIS decompresses 2-8 times slower than RePair and 7-zip, though.
Grammar-based compression is attractive because, unlike Lempel-Ziv, it can be enriched to support fast extraction of arbitrary text substrings. From this perspective and regarding the space-time relation, our experiments show that GCIS is a competitive option when compared to RePair-based extractors, being faster and less space-efficient than the succinct encoding of RePair extractors and slower, but more space-efficient, than the more straightforwardly encoded RePair extractors.
Finally, as a by-product of GCIS, the suffix array of the text can be obtained during the decompression algorithm, faster than decompressing and running on the original text.
All previously discussed features make GCIS especially attractive in scenarios where it is required to support random access on the compressed text. Grammar compression of very large files is challenging with RePair because of its large main memory footprint, for which GCIS offers an interesting alternative. Given its slowness at decompression, the GCIS grammar is best suited as a compressed data structure to be repeatedly accessed without decompressing it completely.
A possible further improvement would be to replace the variant of Nong et al. [2009] used during compression by the more space-efficient SACA-K [Nong 2013] algorithm. This could decrease the working space used during compression.
Grammar-based compressed indexes are of particular interest, all of which are based on RePair Navarro 2010, 2012] . Our next goal is to build those compressed indexes on GCIS instead. This would yield indexes that might be built much faster, using much less memory, for moderate-sized repetitive texts, and that may lead to much more efficient search times. 