Prediction of Compression Ratio for Transform-based Lossy Compression in Time-series Datasets
- Resource Type
- Conference
- Authors
- Moon, Aekyung; Park, Juyoung; Song, Yun Jeong
- Source
- 2022 24th International Conference on Advanced Communication Technology (ICACT) Advanced Communication Technology (ICACT, 2022 24th International Conference on. :142-146 Feb, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Radio frequency
Analytical models
Machine learning
Predictive models
Big Data
Data models
Compressors
IoT(Internet of Thing)
Data analytics
lossy compression
- Language
- ISSN
- 1738-9445
As many IoT devices generate an enormous and varied amount of data that need to be processed in a very short time, storing and processing IoT big data become a huge challenge. While lossy compression can dramatically reduce data volume, finding an optimal balance between volume reduction and information loss is not an easy task. The compression ratio is within a range tolerable by the application is crucial. Motivated by this, we analyze the characteristics of data compressed and present a prediction model about the compression ratio of transformation-based lossy compression algorithms for IoT datasets collected.