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SPARQS: a qualitative spatial reasoning engine

El-Geresy, Baher A. and Abdelmoty, Alia 2004. SPARQS: a qualitative spatial reasoning engine. Knowledge-Based Systems 17 (2-4) , pp. 89-102. 10.1016/j.knosys.2004.03.004

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Abstract

In this paper the design and implementation of a general qualitative spatial reasoning engine (SPARQS) is presented. Qualitative treatment of information in large spatial databases is used to complement the quantitative approaches to managing those systems, in particular, it is used for the automatic derivation of implicit spatial relationships and in maintaining the integrity of the database. To be of practical use, composition tables of spatial relationships between different types of objects need to be developed and integrated in those systems. The automatic derivation of such tables is considered to be a major challenge to current reasoning approaches. In this paper, this issue is addressed and a new approach to the automatic derivation of composition tables is presented. The method is founded on a sound set-theoretical approach for the representation and reasoning over arbitrarily shaped objects in space. A reasoning engine tool, SPARQS, has been implemented to demonstrate the validity of the approach. The engine is composed of a basic graphical interface where composition tables between the most common types of spatial objects are built. An advanced interface is also provided, where users are able to describe shapes of arbitrary complexity and to derive the composition of chosen spatial relationships. Examples of the application of the method using different objects and different types of spatial relationships are presented and new composition tables are built using the reasoning engine.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Qualitative spatial reasoning; Qualitative spatial representation; Spatial composition tables
Publisher: Elsevier
ISSN: 0950-7051
Date of First Compliant Deposit: 6 January 2017
Date of Acceptance: 2 April 2004
Last Modified: 04 Jun 2017 14:05
URI: http://orca-mwe.cf.ac.uk/id/eprint/97234

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