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Issue title: Analysis of Symbolic and Spatial Data
Guest editors: Paula Britox and Monique Noirhomme-Fraiturey
Article type: Research Article
Authors: Tzitzikas, Yannis; *
Affiliations: Institut d'Informatique, Facultés Universitaires Notre Dame de la Paix, Rue Grandgagnage 21, B-5000, Belgium. E-mail: ytz@info.fundp.ac.be | [x] Faculdade de Economia, University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal. E-mail: mpbrito@fep.up.pt | [y] Institut d'Informatique, Facultés Universitaires Notre Dame de la Paix, Rue Grandgagnage, 21, B-5000 Namur, Belgium. E-mail: monique.noirhomme@info.fundp.ac.be
Correspondence: [*] Current address and affiliation: Department of Computer Science, University of Crete, P.O. Box 2208, Heraklion, Crete, GR-714 09, Greece. Institute of Computer Science (ICS), Foundation for Research and Technology – Hellas (FORTH), Science and Technology Park of Crete, Vassilika Vouton, P.O.Box 1385, Heraklio, Crete, GR 7110, Greece. Tel.: +30 2810 391 621; Fax: +30 2810 391 638; E-mail: tzitzik@ics.forth.gr; Web page: http://www.ics.forth.gr/~tzitzik.
Abstract: Although symbolic data tables summarize huge sets of data they can still become very large in size. This paper proposes a novel technique for compressing a symbolic data table using the recently emerged Compound Term Composition Algebra. One advantage 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 efficient algorithms for symbolic data analysis.
Keywords: Symbolic data tables, compression
DOI: 10.3233/IDA-2006-10404
Journal: Intelligent Data Analysis, vol. 10, no. 4, pp. 343-359, 2006
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