Dynamic Data Compression and Security Based Algorithm for Big Sensing Data

Abstract

In this paper, we propose a data compression algorithm named data will be divided into chunks and similarity based compression for the efficient processing of sensing data on the cloud. In current technology, the big challenge lies in the efficient access to storage and evaluating sensing data as it is vital to consider the accuracy of the data. In the traditional compression algorithm, data is considered as a single unit but in our novel approach compression is applied over a partitioned data chunks. Once the sensing data is compressed, in order to regain the original data, we perform some kind of prediction method and restoration algorithm. MapReduce algorithm is incorporated in our approach for providing scalability over the network and similarity checking to verify the correctness of file. The results obtained shows that the technique predominantly increases the efficiency of the data compression with a very less percentage of data loss.