Using Possibilistic Network Models

Uncertainty is a pervasive in real world environment due to vagueness, is associated with the difficulty of making sharp distinctions and ambiguity, is associated with situations in which the choices among several precise alternatives cannot be perfectly resolved. Analysis of large collections of uncertain data is a primary task in the real world applications, because data is incomplete, inaccurate and inefficient. Representation of uncertain data in various forms such as Data Stream models, Linkage models, Graphical models and so on, which is the most simple, natural way to process and produce the optimized results through Query processing. In this paper, we propose the Uncertain Data model can be represented as Possibilistic data model and vice versa for the process of uncertain data using various data models such as possibilistic linkage model, Data streams, Possibilistic Graphs. This paper presents representation and process of Possiblistic Linkage model through Possible Worlds with the use of product-based operator.