Data value storage for compressed semi-structured data

Brian Grieve Tripney, Isla Ross, Francis Wilson, John Wilson

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Growing user expectations of anywhere, anytime access to information require new types of data representations to be considered. While semi-structured data is a common exchange format, its verbose nature makes files of this type too large to be transferred quickly, especially where only a small part of that data is required by the user. There is consequently a need to develop new models of data storage to support the sharing of small segments of semi-structured data since existing XML compressors require the transfer of the entire compressed structure as a single unit.
This paper examines the potential for bisimilarity-based partitioning (i.e. the grouping of items with similar structural patterns) to be combined with dictionary compression methods to produce a data storage model that remains directly accessible for query processing whilst facilitating the sharing of individual data segments.
Study of the effects of differing types of bisimilarity upon the storage of data values identified the use of both forwards and backwards bisimilarity as the most promising basis for a dictionary-compressed structure. A query strategy is detailed that takes advantage of the compressed structure to reduce the number of data segments that must be accessed (and therefore transferred) to answer a query. A method to remove redundancy within the data dictionaries is also described and shown to have a positive effect on memory usage.
Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications
Subtitle of host publicationProceedings of the 24th International Conference on Database and Expert Systems Applications
EditorsH Decker, Lenka Lhotská, Sebastian Link, Josef Basl, A Min Tjoa
Place of PublicationBerlin
Number of pages15
Publication statusPublished - 14 Aug 2013

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


  • data
  • data storage
  • data value


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