Extending F‐Perceptory to model fuzzy objects with composite geometries for GIS

When analyzing spatial issues, geographers are often confronted with many problems with regard to the imprecision of the available information. It is necessary to develop representation and design methods which are suited to imprecise spatiotemporal data. This led to the recent proposal of the F-Perceptory approach. F-Perceptory models fuzzy primitive geometries that are appropriate in representing homogeneous regions. However, the real world often contains cases that are much more complex, describing geographic features with composite structures such as a geometry aggregation or combination. From a conceptual point of view, these cases have not yet been managed with F-Perceptory. This article proposes modeling fuzzy geographic objects with composite geometries, by extending the pictographic language of F-Perceptory and its mapping to the Unified Modeling Language (UML) necessary to manage them in object/relational databases. Until now, the most commonly used object modeling tools have not considered imprecise data. The extended F-Perceptory is implemented under a UML-based modeling tool in order to support users in fuzzy conceptual data modeling. In addition, in order to properly define the related database design, an automatic derivation process is implemented to generate the fuzzy database model.

[1]  Simon Parsons,et al.  A review of uncertainty handling formalisms , 1998, Applications of Uncertainty Formalisms.

[2]  Zongmin Ma,et al.  Advances In Fuzzy Object-oriented Databases: Modeling And Applications , 2004 .

[3]  Zongmin Ma,et al.  Fuzzy XML data modeling with the UML and relational data models , 2007, Data Knowl. Eng..

[4]  R. Pontius QUANTIFICATION ERROR VERSUS LOCATION ERROR IN COMPARISON OF CATEGORICAL MAPS , 2000 .

[5]  Gloria Bordogna,et al.  The Fuzzy Object Oriented Database Management System , 2000 .

[6]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[7]  Philippe Smets,et al.  Imperfect Information: Imprecision and Uncertainty , 1996, Uncertainty Management in Information Systems.

[8]  Y. Bédard VISUAL MODELLING OF SPATIAL DATABASES: TOWARDS SPATIAL PVL AND UML , 1999 .

[9]  Nancy Van Gyseghem,et al.  Imprecision and uncertainty in the UFO database model , 1998 .

[10]  Zongmin Ma,et al.  Conceptual design of fuzzy object‐oriented databases using extended entity‐relationship model , 2001, Int. J. Intell. Syst..

[11]  Herman Akdag,et al.  F-perceptory: an approach for handling fuzziness of spatiotemporal data in geographical databases , 2016 .

[12]  Robert Laurini,et al.  A conceptual framework for geographic knowledge engineering , 2014, J. Vis. Lang. Comput..

[13]  Herman Akdag,et al.  Through a Fuzzy Spatiotemporal Information System for Handling Excavation Data , 2012, AGILE Conf..

[14]  Elena García Barriocanal,et al.  Extending object database interfaces with fuzziness through aspect-oriented design , 2006, SGMD.

[15]  Jonathan Lee,et al.  Modeling Imprecise Requirements with Fuzzy Objects , 1999, Inf. Sci..

[16]  Gerard B. M. Heuvelink,et al.  Propagation of errors in spatial modelling with GIS , 1989, Int. J. Geogr. Inf. Sci..