F-perceptory: an approach for handling fuzziness of spatiotemporal data in geographical databases

Zoghlami, Asma; Runz, Cyril de; Akdag, Herman

doi:10.1504/ijstmis.2016.076761

Cited by 9 publications

(19 citation statements)

References 22 publications

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“…Une autre proposition, basée sur la théorie des ensembles flous, est d'enregistrer une approximation des objets spatiaux flous à l'aide de coupes-alpha (de Runz, 2008 ;Zoghlami et al, 2016). Cette implémentation offre la possibilité de stocker des objets spatiaux flous sans recourir à des développements spécifiques, contrairement à l'approche de Dilo et al (2007), mais les opérations inter-ensembles (union, intersection, etc.…”

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“…Modélisation d'objets spatiaux flous : approche raster (a) Degré d'appartenance à l'ensemble des positions proches d'une ligne à haute tension (b) Degré d'appartenance à l'ensemble des positions situées à plus basse altitude qu'une ligne à haute tension (c) Degré d'appartenance à l'intersection des ensemblestrès proches d'une ligne à haute-tension peuvent, en effet, être situées à des altitudes dépassant fortement le sommet des pylônes avoisinants, démontrant l'intérêt de prendre en compte la différence d'altitude.La seconde implémentation testée est basée sur l'approche utilisée par deRunz (2008) et Zoghlami et al (2016 nécessitant une approche vectorielle. Cependant, aucun système d'information géographique ou plus généralement logiciel permettant la manipulation de données spatiales vectorielles ne permet, du moins à notre connaissance, de manipuler des géométries tridimensionnelles telles que celles que nous souhaitons construire.…”

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Objets et relations spatiales composites et prise en compte du vague pour interpréter un référencement spatial indirect

Bunel¹,

Olteanu‐Raimond²,

Duchêne³

2019

Rev. Int. Geomat.

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RESUME. Cet article propose une méthodologie destinée à transformer une description de position exprimée dans un référentiel indirect en une position absolue dans un référentiel direct. Cette problématique nécessite une formalisation des éléments de localisation relatifs, ici sous la forme d'un modèle en triplets, utilisé ensuite comme base de référencement, et un questionnement sur la prise en compte de l'imperfection du langage naturel dans le processus de modélisation. Nous proposons de faire appel aux objets spatiaux flous et nous comparerons leurs implémentations afin d'identifier l'approche la plus adaptée au contexte du secours en montagne.ABSTRACT. This paper proposes a methodology for transforming a position description expressed relatively in an indirect spatial reference system into an absolute position in a direct spatial reference system. This problem requires (1) to formalize the relative location elements, here in the form of a triplet model, which is used as a basis for the transformation from relative to absolute position, and (2) to investigate how the imperfection of the natural language can be taken into account in the modeling process. We propose to use fuzzy spatial objects and we will compare their implementations to identify the most appropriate approach to our context. MOTS-CLES : Référentiel spatial indirect, logique floue, objets spatiaux vagues, relation spatiale étendue, secours en montagne

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Objets et relations spatiales composites et prise en compte du vague pour interpréter un référencement spatial indirect

Bunel¹,

Olteanu‐Raimond²,

Duchêne³

2019

Rev. Int. Geomat.

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“…The principles of the F‐Perceptory approach were introduced in Zoghlami, De Runz, Akdag, Zaghdoud, and Ben Ghezala () and the formal concepts were defined in Zoghlami, De Runz, and Akdag (). By extending Fuzzy UML, Ma () aimed to manage the object modeling of imprecise spatiotemporal data.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we consider the imprecision of the shape boundary and not the imprecision of attributes (except geometry), identity or topology of geographical objects. Zoghlami et al () and Ma () have effectively described the modelling of fuzzy identity or fuzzy attributes.…”

Section: Introductionmentioning

confidence: 99%

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

Khalfi

Runz

Faïz

et al. 2017

Transactions in GIS

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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. | I NTR OD U CTI ONThe considerable development and use of geographic information, whether in a professional context or for public use, contributes to the more serious consideration of the issue of data quality. The latter has a direct influence on the reliability of spatial analysis and the resulting decisions. The geographic data quality is of major concern to geographers. It is measured through criteria such as geometric precision, completeness, semantic precision, logical consistency, timeliness, etc. The management of geographic data quality also covers their modeling wherein several methods and tools have sprung up to support the challenges of the real needs of the geomatic community. To ensure a better quality of geographic data, it is important to consider its imperfect (imprecise, uncertain, incomplete) nature and to integrate it into the modeling, storing and analysis process (Goodchild, 2007). This article considers a type of imperfection called vagueness according to Fisher (1999) in the GIS community and also called imprecision in the fuzzy set theory 1364 | context, a general focus concerns the modelling and storing of geographical objects with fuzzy boundaries in databases as introduced in Burrough and Frank (1996). This article does not consider error (Pontius, 2000), uncertainty as defined in Foody and Atkinson (2006), ambiguity (Fisher, 1999), inaccuracy or incompleteness (Smets, 1997).The principles of the F-Perceptory approach were introduced in Zoghlami, De Runz, Akdag, Zaghdoud, and Ben Ghezala (2011) and the formal concepts were defined in Zoghlami, De Runz, and Akdag (2016). By extending Fuzzy UML, Ma (2005) aimed to manage the object modeling of imprecise spatiotemporal data. In this article,...

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“…In this paper, we are interested in the conceptual level and especially for the F-Perceptory approach which is a reflection recently conducted by (Zoghlami et al, 2014) and (Zoghlami et al, 2015) for geographic databases modeling supporting the imprecision. Its current version presents some limits on its expression ability to describe complex and multiple geographic objects.…”

Section: Introductionmentioning

confidence: 99%

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

Khalfi

Runz

Faïz

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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F-perceptory: an approach for handling fuzziness of spatiotemporal data in geographical databases

Cited by 9 publications

References 22 publications

Objets et relations spatiales composites et prise en compte du vague pour interpréter un référencement spatial indirect

Objets et relations spatiales composites et prise en compte du vague pour interpréter un référencement spatial indirect

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

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

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