F-perceptory: an approach for handling fuzziness of spatiotemporal data in geographical databases
International Journal of Spatial Temporal and Multimedia Information Systems
In the literature, several studies have focused on introducing fuzzy extensions to the relational and/or object database models in order to store the imprecision. Indeed, on one hand, fuzzy EER and fuzzy UML are both applied for fuzzy object-oriented database modelling. On the other hand, Fuzzy ER is adapted for fuzzy relational database models. All these previous fuzzy conceptual modelling methods are not adapted to fuzzy spatiotemporal data. In this paper, we propose an approach for modelling
... imprecise data in object and relational databases based on the representation of data using connected and normalised fuzzy sets stored via α-cuts. The approach is applied to geographical information systems in order to handle imprecise spatiotemporal data. Paris VI) with the MaLIRE team. Initially focused on information theory and knowledge representation, his scientific orientations evolved to machine learning and cognitive modelling via fuzzy logic. He is also an active researcher in the areas of approximate reasoning, fuzzy set theory, decision-making methods, image retrieval, fuzzy abduction, data mining, robotics and user modelling. From 1980 to 2011, he was an Associate Professor, then Full Professor at Champagne-Ardennes University. A former head of Modeco Research Group and the Research Master RACOR in Champagne-Ardennes University, he is actually member of LIASD Lab, Head of Professional Master CPI and member of the University Paris-Lumières Senat. He has published more than 140 papers in conference proceedings, journals and books. This paper is a revised and expanded version of a paper entitled 'Through a fuzzy spatiotemporal information system for handling excavation data' presented at AGILE 2012, Avignon, France, 2012.