Uncertainty Data Center Model Is Built

In recent years, the importance of uncertain data has been recognized with the rapid development in data gathering and processing in various fields, including economy, military, logistic, finance and telecommunication, etc.In the realm of uncertain data, the challenges aroused from studying uncertain data can be summarized as follows:a helpful mechanism to represent the correlation in uncertain data, an efficient method to support query and analyze lineages on uncertain data, and an approach to make reasoning on uncertain data. Namely, considering the property of uncertain data, to discovery uncertain knowledge represented as a probabilistic graphical model and implied in uncertain data can further accelerate the operations, such as query processing, lineage analysis, decision making, etc.The main idea of this paper is try to discover probabilistic knowledge implied in uncertain data with Bayesian Network(BN) that is a typical Probabilistic Graphical Model(PGM). It is natural to adopt BN to the realm of uncertain data, since BN has great success to represent the uncertain knowledge of traditional certain data considering BN related theories and applications.Dependency analysis is an important and representative method for learning a BN from traditional certain data. In this paper, the BN learned from uncertain data, has nodes that are the columns of the x-relation, and the states of each node that are the set of all possible values from the corresponding columns. The probabilistic dependency relationships in the x-relation are depicted by directed edges between nodes.For introducing BN to uncertain data, contributions in this paper can be summarized as follows:●Theories and methods related to BN require that inputs data must imply all sample data from the Probability Space(PS), from which these data are observed. However, as some data is missing, uncertain data cannot always satisfy this requirement with BN. In this paper, a method of converting uncertain data to satisfy this requirement is presented by decreasing the size of original PS. ●As uncertain data are correlated, a corresponding probability-calculating framework is given, for obtaining the probabilities concerned in the Cl-testes while learning the BN from uncertain data.●As Dependency Model(DM) is the prerequisite for discussing BN, a DM implied in uncertain data is presented by mapping original definitions of DM to the realm of uncertain data.●Considering the properties of uncertain data, like data missing and data correlating, etc, it is not right to apply traditional BN learning method to uncertain data directly. By replacing critical components of traditional BN learning method, a sound and feasible BN learning method for uncertain data is give.