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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/116051...
Part of book or chapter of book . 2006 . Peer-reviewed
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DBLP
Conference object . 2017
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Compressing XML Documents Using Recursive Finite State Automata

Authors: Subramanian, Hariharan; Shankar, Priti;

Compressing XML Documents Using Recursive Finite State Automata

Abstract

We propose a scheme for automatically generating compressors for XML documents from Document Type Definition(DTD) specifications. Our algorithm is a lossless adaptive algorithm where the model used for compression and decompression is generated automatically from the DTD, and is used in conjunction with an arithmetic compressor to produce a compressed version of the document. The structure of the model mirrors the syntactic specification of the document. Our compression scheme is on-line, that is, it can compress the document as it is being read. We have implemented the compressor generator, and provide the results of experiments on some large XML databases whose DTD's are specified. We note that the average compression is better than that of XMLPPM, the only other on-line tool we are aware of. The tool is able to compress massive documents where XMLPPM failed to work as it ran out of memory. We believe the main appeal of this technique is the fact that the underlying model is so simple and yet so effective.

Country
India
Related Organizations
Keywords

Computer Science & Automation (Formerly, 005, School of Automation)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Average
Top 10%
Average
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