Title: A Review On Various Lossless Data Compression Technique For Machine Learning And Iot Data
Abstract: Compression is the most important technique during the data transmission from one placeto another place. Using data compression, the volume of a file can be reduced which willhelp to decrease the need of new hardware, improve database performance, speed upbackups, Provide more secure storage. Compression has two different types whichclassified as either lossy or lossless. Lossless compression methodology compresses thedata to be transferred without any missing in original data. Using this compression theinformation should not get changed at the place of destination. For example, many sensorparameters can be sensed using sensors placed in various places, which data should becollected 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 withoutany data loss. These kinds of methodology can be used for the secure communication whileprocessing the data. There are many lossless data compression algorithms are available forus to performing the data compression techniques like Huffman coding, Run lengthEncoding techniques, etc., In this paper we are going to discuss about how datacompression techniques will take exciting role in era of rich data used in Machinelearning, IoT and so on. We are going to compare algorithms based on energy,performance, encryption and decryption during compression which algorithm will producebetter result for these kinds of techniques.
Publication Year: 2021
Publication Date: 2021-06-01
Language: en
Type: review
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