Modelling and computing with imprecise and uncertain properties in object bases

Abstract

Although fuzzy set and probability theories are complementary for dealing with pervasive imprecision and uncertainty in real world problems, object-oriented database models combining the relevance and strength of both the theories appear to be sporadic. This paper introduces our extension of Eiter et al.'s probabilistic object base model with two key features: (1) uncertain and imprecise attribute values are represented as probability distributions on a set of fuzzy set values; and (2) class methods with uncertain and imprecise input and output arguments are formally integrated into the new model. 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 defined. Furthermore, the soft computing paradigm needs to have real systems implemented to be useful in practice. This paper also presents our development of FPDB4O as a management system for fuzzy and probabilistic object bases of the proposed model. © 2008 Springer-Verlag Berlin Heidelberg.