Extending probabilistic object bases with uncertain applicability and imprecise values of class properties

Abstract

Although there have been many fuzzy object-oriented data models proposed, and a bit less for probabilistic ones, models combining the relevance and strength of both fuzzy set theory and probability theory appear to be sporadic. This paper introduces our extension of Eiter et al.'s probabilistic object base model with three key features: (1) uncertain and imprecise attribute values are represented as probability distributions on a set of fuzzy set values; (2) class methods with uncertain and imprecise input and output arguments are formally integrated into the new model; and (3) applicability of class properties and their inheritance can be uncertain. A probabilistic interpretation of relations on fuzzy set values is proposed for their combination with probability degrees. Then the syntax and semantics of fuzzy-probabilistic object base schemas, instances, and selection operation are presented. © 2007 IEEE.