상세정보
학술대회
Prediction of Compression Ratio for Transform-based Lossy Compression in Time-series Datasets
- 발행일
- 202202
- 출처
- International Conference on Advanced Communications Technology (ICACT) 2022, pp.142-146
- 협약과제
- 22ID1500, 유연인쇄전자 신전자산업 기술개발, 문애경
- 초록
- 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 제안 키워드
- Big Data, Compression Algorithm, Data Volume, Information Loss, IoT Devices, Lossy Compression, Short time, Time series, Volume reduction, compression ratio, prediction model