Abstract
Linear Constraints may constitute a good alternative to the widely used vector model for the representation of geometric objects. Linear Constraints are sets of inequalities of convex polygons and are simpler but more powerful than the relational model. Queries on thematic data and spatial data are expressed by operations on inequalities. This paper presents the basics of linear constraint databases. It explores whether Linear Constraints are practically viable for the use in GIS. The evaluation is based on:
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how much storage is used,
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how data collections are processed, and
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if the most important operations of GIS can be implemented efficiently.
We conclude that the Linear Constraints are a promising representation of spatial data.
This work is part of the cooperation within the Esprit CONTESSA Working Group.
Work supported by INRIA, FNRS and ULB.
Work supported in part by NSF STC grant SBR-8920230 and INRIA.
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Haunold, P., Grumbach, S., Kuper, G., Lacroix, Z. (1997). Linear constraints: Geometric objects represented by inequalitiesl. In: Hirtle, S.C., Frank, A.U. (eds) Spatial Information Theory A Theoretical Basis for GIS. COSIT 1997. Lecture Notes in Computer Science, vol 1329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63623-4_65
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DOI: https://doi.org/10.1007/3-540-63623-4_65
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