Jérôme Dantan

A Formal Model to Compute Uncertain Continuous Data

Current researches in the domain of Information and Communication Technologies describe and extend the existing formalisms to develop systems that compute uncertain data. Indeed, handling uncertain data is a great challenge for complex systems. In this article, we provide a formal model to compute such data rigorously. Such quantities may be interpreted as either possible or probable values, added to their interdependencies. For this, the algebraic structure we defined is a vector space. We then provide a particular way for mixing such continuous quantities.

A Systemic Meta-Model for Socio-Environmental Systems

We propose a systemic meta-model for the sustainable simulation of socio-environmental complex systems. The approach presented integrates data uncertainty management, for both representing and manipulating rigorously quantities which may have a finite number of possible or probable values with their interdependencies. We also provide an operationalization of such models for both data retrieving, via an object-relational mapping, and model simulation, via series of triples, which are linked to examples in the field of agriculture.

Taking Account of Uncertain, Imprecise and Incomplete Data in Sustainability Assessments in Agriculture

To manipulate incomplete, uncertain and hypothetic information about farms, the future evaluation software in the agriculture domain will have to make computations and combine fuzzy numerical values. The approach we propose relies on combination of incomplete, uncertain and hypothetic object values based on particular application of the Dempster-Shafer evidence theory. We then propose the integration of this approach in classic object oriented programming via the implementation of a C++ library.

A Goal-Oriented Meta-Model for Scientific Research

In many research domains, studies need to be addressed in a multidisciplinary manner: each expert deals with a particular aspect of the problem. It may be useful for experts to take into account new data, share partial results, and update their own indicators and models, in order to take advantage of new measures and updated indexes in real time. For this, an easily-understandable knowledge model for any raw data source, statistical operator, indicator or business process to be available for experts, is needed. In this paper, we propose a goal-oriented meta-model, to index and reuse treatments and an extension of existing semantic Web standards to index goal-oriented services and assist and/or automate their selection. These features enable capabilities for interoperability and information exchange between three layers of knowledge: goal, domain and data layers. An application with an existing ontology of the agriculture domain and farm durability indicators will be proposed.