A PERSONALIZED SCHEME FOR INCOMPLETE AND DUPLICATE INFORMATION HANDLING IN RELATIONAL 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. This work proposes the renovated concept of partial information handling with complete prediction model, which handle incompleteness in relational databases. 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 work 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 operator also allows them to state a strategy 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. The present work also analyze how relational algebra operators and PIP operators interact with one another. This also handles the COALESCE function using the CIP operator.