Design and analysis for improving reliability and accuracy of big-data based peripheral control through IoT

In recent years, Big Data analytics has become a popular research in computer science and other related data mining areas. It is very important research area in new trends and also creates enormous vibration in technocrats. It is not only vibrant in computer science but also spread into many other diversified areas. In many industrial applications the data storage and retrieval after analysis become a primary task in frequent situations. But achieving at correct time and accurate data become a tedious job at data bases. As the data base sizes are large and extended virtually it is also become difficult to provide security to the data. The data can be quickly accessed with multiplexed memory data base and dedicated, unidirectional storage devices. The accuracy and through put can be improved with parallel processing elements which are involved in super scalar architectures. In the present paper queues are interconnected for faster processing and multiplexed and direct access storage devices are used to access past data for parameter analysis and fetching predefined data values.