A Review On Various Lossless Data Compression Technique For Machine Learning And Iot Data

Document Type : Primary Research paper


1 Assistant Professor ,Dept of CSE, Bannari Amman Institute of Technology, Sathyamangalam, India

2 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.


Volume 12, Issue 3 - Serial Number 3
ICMMNT-2021 International Virtual Conference on Materials, Manufacturing and Nanotechnology, 30th June, 2021.
June 2021
Pages 1261-1271