This paper introduces the main characteristics of our fuzzy temporal semantic model. It deals with (i) the problem of temporal data modeling under EFSM (Enhanced Fuzzy Semantic Model) to ensure the history of the values of attributes and/or objects, the values of partial degrees of membership and the values of degrees of membership, (ii) the impact of data evolution on the calculation of these degrees of membership.
This paper extends FSM, a recently proposed semantic data model that supports fuzziness, imprecision and uncertainty of real-world. More precisely, the paper proposes four new concepts, decisional grouping, inhibition, multiplicity and selection, which allows enhancing the modeling of real-world applications. It integrates these concepts in FSM by the definition of new decision rules.
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