On compression of parse trees

Tarhio, Jorma

doi:10.1109/spire.2001.989759

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In particular, zero‐order prediction depending on the structural context, plus arithmetic coding, is used for the tags. Other grammar‐based techniques can be found in Tarhio (2001), as well as in XML‐Xpress, a commercial software (http://www. ictcompress.com) that compresses well when the DTD is known.…”

Section: Related Workmentioning

confidence: 99%

Lempel‐Ziv compression of highly structured documents

Adiego

Navarro

Fuente

2007

J. Am. Soc. Inf. Sci.

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The authors describe Lempel-Ziv to Compress Structure (LZCS), a novel Lempel-Ziv approach suitable for compressing structured documents. LZCS takes advantage of repeated substructures that may appear in the documents, by replacing them with a backward reference to their previous occurrence. The result of the LZCS transformation is still a valid structured document, which is human-readable and can be transmitted by ASCII channels. Moreover, LZCS transformed documents are easy to search, display, access at random, and navigate. In a second stage, the transformed documents can be further compressed using any semistatic technique, so that it is still possible to do all those operations efficiently; or with any adaptive technique to boost compression. LZCS is especially efficient in the compression of collections of highly structured data, such as extensible markup language (XML) forms, invoices, e-commerce, and Web-service exchange documents. The comparison with other structure-aware and standard compressors shows that LZCS is a competitive choice for these type of documents, whereas the others are not well-suited to support navigation or random access. When joined to an adaptive compressor, LZCS obtains by far the best compression ratios.

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Section: Related Workmentioning

confidence: 99%

Lempel‐Ziv compression of highly structured documents

Adiego

Navarro

Fuente

2007

J. Am. Soc. Inf. Sci.

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show abstract

“…In particular, zero-order prediction depending on the structural context, plus arithmetic coding, is used for the tags. Other grammar-based techniques can be found in (Tarhio, 2001), as well as in XML-Xpress, a commercial software (http://www.ictcompress.com) that compresses well when the DTD is known.…”

Section: Structured Text Compressionmentioning

confidence: 99%

Using structural contexts to compress semistructured text collections

Adiego

Navarro

Fuente

2007

Information Processing & Management

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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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“…When compressing the text T , the encoder compresses the derivation tree of T from the start symbol of the grammar G. Katajainen et al [11] and Cameron [2] initiated this approach for compressing program source codes, and Eck et al [5] and Evans [6] followed them. Lake [12] and Tarhio [28] strengthened their work by combining with the partial pattern matching (PPM) method [4]. Depending on the target domains, various methods are proposed, especially for XML files [3,14].…”

Section: Introductionmentioning

confidence: 99%

Grammar compression with probabilistic context-free grammar

Naganuma¹,

Hendrian²,

Yoshinaka³

et al. 2020

Preprint

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We propose a new approach for universal lossless text compression, based on grammar compression. In the literature, a target string T has been compressed as a context-free grammar G in Chomsky normal form satisfying L(G) = {T }. Such a grammar is often called a straight-line program (SLP). In this paper, we consider a probabilistic grammar G that generates T , but not necessarily as a unique element of L(G). In order to recover the original text T unambiguously, we keep both the grammar G and the derivation tree of T from the start symbol in G, in compressed form. We show some simple evidence that our proposal is indeed more efficient than SLPs for certain texts, both from theoretical and practical points of view.

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On compression of parse trees

Abstract: We

Cited by 5 publications

References 15 publications

Lempel‐Ziv compression of highly structured documents

Lempel‐Ziv compression of highly structured documents

Using structural contexts to compress semistructured text collections

Grammar compression with probabilistic context-free grammar

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