Title: Assessment of candidate information models for granular computing

Authors: Steven A. Demurjian

Addresses: Department of Computer Science and Engineering, School of Engineering, University of Connecticut, 371 Fairfield Way, U-2155, Storrs, Connecticut, 06269-2155, USA

Abstract: Granular computing (GrC) is an emerging discipline of information theory that strives to allow reasoning and analysis based on varying levels of information granularity (from fine to coarse). In GrC, the entire information universe can be organised based on many different criteria, allowing the information to be abstracted, aggregated, classified, generalised and so on, based on various characteristics (e.g., data similarity, operational usage, etc.). As a result, GrC relies on information models to describe the universe, the elements of the universe and the composition of each element. In this paper, candidate information models for GrC are explored and assessed, including: the attribute-based data model, the relational data model, the functional data model and the extensible markup language. This includes a description of these models in terms of their concepts, data definition and data manipulation and an analysis of the suitability in support of GrC.

Keywords: granular computing; GrC; data modelling; information modelling; attribute-based data models; ABDM; data tables; information tables; extensible markup language; XML; relational data models; RDM; information granularity; functional data models; data definition; data manipulation.

DOI: 10.1504/IJGCRSIS.2009.026722

International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2009 Vol.1 No.1, pp.1 - 20

Published online: 24 Jun 2009 *

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