An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Inference Using Bayesian Network in PPDM and System Reliability <s> This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Inference Using Bayesian Network in PPDM and System Reliability <s> In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidentiality of association rules, are based on the assumptions while safeguarding susceptible information rather than recognition of insightful items. Therefore, it is time to go one step ahead in order to remove such assumptions in the protection of responsive information especially in XML association rule mining. Thus, we focus on this central and highly researched area in terms of generating XML association rule mining without arguing on the disclosure risks involvement in such mining process. Hence, we described the identification of susceptible items in order to hide the confidential information through a supervised learning technique. These susceptible items show the high dependency on other items that are measured in terms of statistical significance with Bayesian Network. Thus, we proposed two methodologies based on items probabilistic occurrence and mode of items. Additionally, all this information is modeled and named PPDM (Privacy Preservation in Data Mining) model for XARs. Furthermore, the PPDM model is helpful for sharing markets information among competitors with a lower chance of generating monopoly. Finally, PPDM model introduces great accuracy in computing sensitivity of items and opens new dimensions to the academia for the standardization of such NP-hard problems. <s> BIB002
To demonstrate an application of BN of system behavior, a BN of five components interacting in a system is shown in Fig. 3 . In this network, a relationship among components can be observed. Moreover, degrees of these relationships for each component are expressed as scores of entire possible candidate parents set in BIB001 . The top most nodes/components A, B and D are conditionally independent. However, the overall system behavior is dependent because of outgoing edges in the network from A, B and D to C and E. Therefore, the prior probabilities of these nodes/components must be known in advance based on historical data or with the help of domain expert. Moreover, components/nodes C, E are conditionally dependent and their probabilities should be calculated using Bayes theorem. The overall system reliability can be computed by including the components with 'Success' probability to get system behavior. BN is used in PPDM for hiding sensitive XML Association Rules as in BIB002 . The main objective of using BN in PPDM is to predict sensitive node(s) in large datasets like system behavior node. The purpose of finding sensitive node is to hide sensitive XML Association Rules. Consequently, BN supports and provides quite reasonable results in minimizing the disclosure risk BIB002 .