Improvement of the F-Perceptory Approach Through Management of Fuzzy Complex Geographic Objects

Abstract

ABSTRACT:In the real world, data is imperfect and in various ways such as imprecision, vagueness, uncertainty, ambiguity and inconsistency. For geographic data, the fuzzy aspect is mainly manifested in time, space and the function of objects and is due to a lack of precision. Therefore, the researchers in the domain emphasize the importance of modeling data structures in GIS but also their lack of adaptation to fuzzy data. The F-Perceptory approachh manages the modeling of imperfect geographic information with UML. This management is essential to maintain faithfulness to reality and to better guide the user in his decision-making. However, this approach does not manage fuzzy complex geographic objects. The latter presents a multiple object with similar or different geographic shapes. So, in this paper, we propose to improve the F-Perceptory approach by proposing to handle fuzzy complex geographic objects modeling. In a second step, we propose its transformation to the UML modeling.

A New Methodology for Storing Consistent Fuzzy Geospatial Data in Big Data Environment

“…Many suggestions have been developed for modeling geographical data, like CONGOO, POLLEN, and PERCEPTORY, but they did not consider their fuzzy nature. More recently, based on Fuzzy UML and Perceptory pictograms, a modeling approach called F-Perceptory was defined in order to model fuzzy geospatial data ( [11], [34] ).…”

“…It allows expressing modeling needs in terms of collection, alternative, facultative, and multiple geometries. They are described in detail in [34]. The Fuzzy GeoJSON schema introduces the JSON structure of these composite fuzzy features associated with the necessary integrity constraints.…”

“…The collection geometry describes a spatial object that is an aggregation of one simple shape type. For example, a street in the real world might consist of several road segments [34]. Based on the type shape, the Fuzzy GeoJSON schema distinguishes three aggregation categories: (1) MultiFuzzyPoint object: a collection of fuzzy point geometries, (2) MultiFuzzy-Polygon: a collection of fuzzy polygon geometries, and (3) MultiFuzzyLineString: a collection of fuzzy line geometries.…”

“…According to [34], the fuzzy alternative geometry introduces objects that can be represented by two possible spatial representations, but each object has only one of the two, never both. For example, a shopping center can be either markets or malls.…”