Optimized Data Management Across Large Datasets

The current trend for data management system is to use various algorithms or search engines to extract and retrieve the data collected and stored in the preexisting Data Management System. As the volume of the available information stored in the DMS is increasing regularly data extraction and retrieval is significantly challenging and taking longer and causing delays to the end users. This paper describes a new method which extends the existing definitions of modules and by introducing novel properties of robustness to optimize the data management across large datasets. Through investigations are carried in the setting of description logics which underlie modern ontology based system. This method meets the performance requirements with the highest possible system reliability and the most reasonable systems cost. Reference data is extracted as per the application needs and extra constraints are applied to manage the data using the resulting schema. The global query answering method is used to identify relevant data within the distributed data set consisting of the data set of the module based data. The flexibility associated with this system makes the data maintenance effective and efficient. Users are given authentication so as to allow only the registered users.