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Conference Paper
Prediction of Compression Ratio for Transform-based Lossy Compression in Time-series Datasets
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- Authors
- Aekyung Moon, Juyoung Park, Yun Jeong Song
- Issue Date
- 2022-02
- Citation
- International Conference on Advanced Communications Technology (ICACT) 2022, pp.142-146
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Big-data, Compression Algorithm, Data Volume, Information loss, IoT devices, Short time, Time series, Volume reduction, compression ratio, lossy compression, prediction model