The XML handbook
XMill: an efficient compressor for XML data
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Machine Learning
Learning semantic functions of attribute grammars
Nordic Journal of Computing
Introduction to Attributed Grammars
Proceedings on Attribute Grammars, Applications and Systems
Acta Cybernetica
Towards a Standard Schema for C/C++
WCRE '01 Proceedings of the Eighth Working Conference on Reverse Engineering (WCRE'01)
CodeCrawler - Lessons Learned in Building a Software Visualization Tool
CSMR '03 Proceedings of the Seventh European Conference on Software Maintenance and Reengineering
Compressing XML with Multiplexed Hierarchical PPM Models
DCC '01 Proceedings of the Data Compression Conference
XGRIND: A Query-Friendly XML Compressor
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Columbus - Reverse Engineering Tool and Schema for C++
ICSM '02 Proceedings of the International Conference on Software Maintenance (ICSM'02)
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Nowadays, one of the most common formats for storing information is XML. The biggest drawback of XML documents is that their size is rather large compared to the information they store. XML documents may contain redundant attributes, which can be calculated from others. These redundant attributes can be deleted from the original XML document if the calculation rules can be stored somehow. In an Attribute Grammar environment there is an analog description for these rules: semantic rules. In order to use this technique in an XML environment we defined a new metalanguage called SRML. We have developed a method, which enables us to use this SRML metalanguage for compacting XML documents. After compaction it is possible to use XML compressors to make the compacted document much smaller. By using this combined approach we could achieve a significant size reduction compared to the compressed size of the XML specific compressors. This article extends the method published earlier to provide the possibility of automatically generating rules using machine learning techniques, with which it can find relationships between attributes which might not have been noticed by the user beforehand.