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Spatial Pictogram Enhanced Conceptual Data Models and Their Translation to Logical Data Models
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Spatial Pictogram Enhanced Conceptual Data Models and Their Translation to Logical Data Models
Shashi Shekhar5 , Ranga Raju Vatsavai5, 6 , Sanjay Chawla5 and Thomas E. Burk6 
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Computer Science Department, University of Minnesota, EE/CS 4-192, 200 Union St. SE., Minneapolis, MN, 55455 |
| (6) |
Department of Forest Resources, University of Minnesota, 115 Green Hall, St. Paul, MN, 55108 |
Abstract
The successful development of any geographic information system project needs the careful design and implementation of spatial
databases via conceptual and logical data-modeling. This involves understanding the underlying spatial data model, spatial
data types and operators, spatial query languages and spatial indexing techniques. Conventional entity relationship diagrams
have limitations for conceptual spatial data-modeling, since they get cluttered with numerous spatial relationships. In addition
the logical data model gets cluttered with redundant tables representing materialization of the M:N spatial relationships.
In this paper we present an extension to ER diagrams using pictograms for entities and as well as relationships. This approach
effectively reduces the cluttering, as spatial relationships will become implicit. We have provided a complete grammar using
“yacc” like syntax to translate the pictogram-extended ER diagram into a SQL3-level logical data model using OGIS-standard
spatial data types.
Keywords Spatial Databases - Pictograms - SQL3 - OGIS - Entity-Relationship Diagrams - UML - Syntax Directed Translation
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