Data Integration Progression in Large Data Source Using Mapping Affinity

Many kind of pattern integration need to be effectively analyzed in large data which require extremely accurate pattern. Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. Existing patterns integration extracts low quality of pattern mapping in large databases and the systems focus only on identifying useful patterns at the attribute-value level. We propose a generalized technique to enable seamless integration of Multiple Data Sources It improves the quality of pattern reorganization significantly. Finally, experiments are conducted on few datasets, and the results of the experiments show that our method is useful and efficient.