Assistant Professor ,Dept of CSE, Bannari Amman Institute of Technology, Sathyamangalam, India
Professor, Dept of CSE, CMR Engineering College, Telangana, Hyderabad, Tndia.
Compression is the most important technique during the data transmission from one place to another place. Using data compression, the volume of a file can be reduced which will help to decrease the need of new hardware, improve database performance, speed up backups, Provide more secure storage. Compression has two different types which classified as either lossy or lossless. Lossless compression methodology compresses the data to be transferred without any missing in original data. Using this compression the information should not get changed at the place of destination. For example, many sensor parameters can be sensed using sensors placed in various places, which data should be collected and should reach the server without any data loss. In machine learning domain, many data are collected in day by day manner these data should be communicated without any data loss. These kinds of methodology can be used for the secure communication while processing the data. There are many lossless data compression algorithms are available for us to performing the data compression techniques like Huffman coding, Run length Encoding techniques, etc., In this paper we are going to discuss about how data compression techniques will take exciting role in era of rich data used in Machine learning, IoT and so on. We are going to compare algorithms based on energy, performance, encryption and decryption during compression which algorithm will produce better result for these kinds of techniques.