Lempel‐Ziv compression of highly structured documents

Adiego, Joaquín; Navarro, Gonzalo; Fuente, Pablo de la

doi:10.1002/asi.20496

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…The operations Access, Depth, Height, Size, Parent, Firstchild, and NextSibling all take a node v in T as input 1 and return its label, its depth, its height, the size of its subtree, its parent, its first child, and its sibling to the right, respectively. The LevelAncestor returns an ancestor at a specified distance from v, and NCA returns the nearest common ancestor to a given pair of nodes.…”

Section: Our Resultsmentioning

confidence: 99%

“…Its size can be exponentially smaller than n. DAG representation of trees are broadly used for identifying and sharing common subexpressions, e.g., in programming languages [25] and binary decision diagrams [24]. Compression based on DAGs has also been studied more recently in [7,14,22] and a Lempel-Ziv analog of subtree repeats was suggested in [1]. It is possible to support navigational queries [6] and path queries [7] directly on the DAG representation in logarithmic time.…”

Section: Previous Workmentioning

confidence: 99%

“…2. In all other cases, we continue the search in the child cluster containing x 1 C , and compute the new local preorder number for x 1 C .…”

Section: Firstchild Level Ancestor Parent and Ncamentioning

confidence: 99%

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Tree compression with top trees

Bille

Gørtz

Landau

et al. 2015

Information and Computation

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We introduce a new compression scheme for labeled trees based on top trees [3]. Our compression scheme is the first to simultaneously take advantage of internal repeats in the tree (as opposed to the classical DAG compression that only exploits rooted subtree repeats) while also supporting fast navigational queries directly on the compressed representation. We show that the new compression scheme achieves close to optimal worst-case compression, can compress exponentially better than DAG compression, is never much worse than DAG compression, and supports navigational queries in logarithmic time.

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Section: Our Resultsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

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Tree compression with top trees

Bille

Gørtz

Landau

et al. 2015

Information and Computation

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show abstract

“…The research [8] presents a new lossless image compression algorithm based on a linear prediction, modified Golomb-Rice code family, and a very fast prediction error modeling method for large images, for natural images of high bit depths, and for noisy images. Lempel-Ziv to Compress Structure [9] focuses on the compression of structured text such as highly structured data including XML forms, invoices, e-commerce, and Web-service exchange documents. The research in [10] describes the operating principle of content-addressable memory as a compression accelerator and the control of the Lempel-Ziv data compression circuit as an application for an explanation of the hardware.…”

Section: Other Compression Methodsmentioning

confidence: 99%

LZCode based compression method for effectively using the memory of embedded devices

Lee

Kim

Choi

et al. 2012

Int J Communication

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Recently, with the rapid growth of embedded devices such as smart phones, tablet PC, and so on, the research on the effective use of flash memory widely used in embedded devices has been increasing. The growth is expected to continue more rapidly and the need for research will increase more. Therefore, there is a coming need for the effective use of main memory with flash memory in embedded devices. Essentially, all program codes have to be loaded into main memory before they can be executed. Then, the time for loading them into main memory is to be the sum of time for reading compression data from flash memory and for decompressing the data by a compressor residing in the main memory. Thus, the smaller the size of the compressor can make the utilization of main memory more effective. In this paper, we present our research on a lossless compression algorithm for reducing the size of the compressor while still maintaining compression rate and speed similar to LZCode suitable for embedded devices.Hyunchul Lee is currently an MS student at the Knowledge Information Security Department, Ajou University, Suwon, Korea. His main research interests include mobile computing, ubiquitous computing, mobile security, and image compression/ decompression algorithm. . His main research interests include mobile computing, ubiquitous computing, scalable distributed database system, mobile applications with smart phones, mobile security, data mining, and bioinformatics.

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“…LZCS (Adiego et al, 2004;Adiego et al, 2006) This compressor uses an idea similar to LZ77, restricted to replacing whole subtrees. This permits the compressed text being accessed, searched, and navigated without decompressing it.…”

Section: Structured Text Compressionmentioning

confidence: 99%

Using structural contexts to compress semistructured text collections

Adiego

Navarro

Fuente

2007

Information Processing & Management

Self Cite

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We describe a compression model for semistructured documents, called Structural Contexts Model (SCM), which takes advantage of the context information usually implicit in the structure of the text. The idea is to use a separate model to compress the text that lies inside each different structure type (e.g., different XML tag). The intuition behind SCM is that the distribution of all the texts that belong to a given structure type should be similar, and different from that of other structure types.We mainly focus on semistatic models, and test our idea using a word-based Huffman method. This is the standard for compressing large natural language text databases, because random access, partial decompression, and direct search of the compressed collection is possible. This variant, dubbed SCMHuff, retains those features and improves Huffman's compression ratios.We consider the possibility that storing separate models may not pay off if the distribution of different structure types is not different enough, and present a heuristic to merge models with the aim of minimizing the total size of the compressed database. This gives an additional improvement over the plain technique. The comparison against existing prototypes shows that, among the methods that permit random access to the collection, SCMHuff achieves the best compression ratios, 2-4% better than the closest alternative.From a purely compression-aimed perspective, we combine SCM with PPM modeling. A separate PPM model is used to compress the text that lies inside each different structure type. The result, SCMPPM, does not permit random access nor direct search in the compressed text, but it gives 2-5% better compression ratios than other techniques for texts longer than 5 megabytes.

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Lempel‐Ziv compression of highly structured documents

Cited by 8 publications

References 29 publications

Tree compression with top trees

Tree compression with top trees

LZCode based compression method for effectively using the memory of embedded devices

Using structural contexts to compress semistructured text collections

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