Uncertainty is inherent in various applications, such as Sensor Networks, Large Datasets, Medicine,
Mobile Networks, Biomedical and Clinical Data, Social and Economical Research. Uncertain data poses
significant challenges for data analytic tasks. Analysis of large collections of uncertain data is a primary
task in these applications, because data is vague, ambiguous, incomplete, and inefficient. In this paper, we
investigate the fundamental problem of analysis and representation of uncertain data objects for
processing. Representation of uncertain data in various approaches such as Probabilistic based,
Possibilistic based, plausibility based theory and so on, in terms of Data Streams, Linkage models, DAG
models, etc. Among these Possibilistic data models are 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 a Min-based symmetry Possibilistic data model and vice versa using linkage
data model through possible Worlds.