MIN-BASED SYMMETRY POSSIBILISTIC NETWORK MODEL FOR REPRESENTATION OF UNCERTAIN DATAMODELS

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.