An Algebraic Method for Compressing Very Large Symbolic Data Tables

Yannis Tzitzikas

An Algebraic Method for Compressing Very Large Symbolic Data Tables

Yannis Tzitzikas

Faculte d'informatique

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Résumé

Although symbolic data tables summarize huge sets of data they can still become very large in size. This paper proposes a method for compressing a symbolic data table using the recently emerged Compound Term Composition Algebra. One charisma of CTCA is that the closed world hypotheses of its operations can lead to a remarkably high 'compression ratio'. The compacted form apart from having much lower storage space requirements, it allows designing more e±cient algorithms for symbolic data analysis.

langue originale	Anglais
titre	Proceedings of the Workshop on Symbolic and Spatial Data Analysis (SSDA) of ECML/PKDD 2004
Etat de la publication	Publié - 2004

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title = "An Algebraic Method for Compressing Very Large Symbolic Data Tables",

abstract = "Although symbolic data tables summarize huge sets of data they can still become very large in size. This paper proposes a method for compressing a symbolic data table using the recently emerged Compound Term Composition Algebra. One charisma of CTCA is that the closed world hypotheses of its operations can lead to a remarkably high 'compression ratio'. The compacted form apart from having much lower storage space requirements, it allows designing more e±cient algorithms for symbolic data analysis.",

author = "Yannis Tzitzikas",

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language = "English",

booktitle = "Proceedings of the Workshop on Symbolic and Spatial Data Analysis (SSDA) of ECML/PKDD 2004",

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AB - Although symbolic data tables summarize huge sets of data they can still become very large in size. This paper proposes a method for compressing a symbolic data table using the recently emerged Compound Term Composition Algebra. One charisma of CTCA is that the closed world hypotheses of its operations can lead to a remarkably high 'compression ratio'. The compacted form apart from having much lower storage space requirements, it allows designing more e±cient algorithms for symbolic data analysis.

M3 - Conference contribution

BT - Proceedings of the Workshop on Symbolic and Spatial Data Analysis (SSDA) of ECML/PKDD 2004

ER -

An Algebraic Method for Compressing Very Large Symbolic Data Tables

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