Data integration and data cleansing are particularly relevant for military applications where trustable data can make a difference in life-threatening conditions. This chapter proposes a novel and robust mechanism for information validation and amendment in databases where certainty in information is critical. It describes the set of experiments conducted for the comparison of performance between different methods on different datasets. The chapter includes a description of some available methods to complete databases and a more complete description of the autoregressive Bayesian network. The knowledge in a process using Bayesian networks can be represented with two elements: the structure of the network, and the parameters. Dynamic Bayesian networks (DBNs) are an attempt to add the temporal dimension into the Bayesian network model. Autoregressive Bayesian networks are a simplified variant of DBNs. Results suggest that the interplay between the variable's characteristics in the dataset dictates the most beneficial reconstruction option.