A Novel Technique For Duplicate And Incomplete Information Handling In Large Databases

Missing data replacement is a crucial process in most real world databases. Due to the tremendous improvement of data management, users of such database can effectively manage the incompleteness using their customized policies.The incomplete data management brings a new challenge which is the data duplication. The Customized Information Prediction Policies with effective index method has been proposed in this paper for handling missing data. Different users in the real world have different ways in which they want to handle incompleteness. The CIP operators suggest the best match to replace the null value, and this also allows them to specify a policy that matches their attitude to risk and their knowledge of the application. Using the same strategy DIP operators has been introduced to handle duplicate data's in the relational database. Using the Autoregressive HMM the system improves the prediction method. The CIP manages all data and policies using PQ Index structures, which is known as Priority Queue based Index.