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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.06713 |
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| _version_ | 1866912243303579648 |
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| author | Sun, Guoyou Karras, Panagiotis Zhang, Qi |
| author_facet | Sun, Guoyou Karras, Panagiotis Zhang, Qi |
| contents | The distributed data infrastructure in Internet of Things (IoT) ecosystems requires efficient data-series compression methods, along with the ability to feed different accuracy demands. However, the compression performance of existing compression methods degrades sharply when calling for ultra-accurate data recovery. In this paper, we introduce SHRINK, a novel highly accurate data compression method that offers a higher compression ratio and also lower runtime than prior compressors. SHRINK extracts data semantics in the form of linear segments to construct a compact knowledge base, using a dynamic error threshold that it adapts to data characteristics. Then, it captures the remaining data details as residuals to support lossy compression at diverse resolutions as well as lossless compression. As SHRINK identifies repeated semantics, its compression ratio increases with data size. Our experimental evaluation demonstrates that SHRINK outperforms state-of-art methods with an up to threefold improvement in compression ratio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06713 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SHRINK: Data Compression by Semantic Extraction and Residuals Encoding Sun, Guoyou Karras, Panagiotis Zhang, Qi Distributed, Parallel, and Cluster Computing The distributed data infrastructure in Internet of Things (IoT) ecosystems requires efficient data-series compression methods, along with the ability to feed different accuracy demands. However, the compression performance of existing compression methods degrades sharply when calling for ultra-accurate data recovery. In this paper, we introduce SHRINK, a novel highly accurate data compression method that offers a higher compression ratio and also lower runtime than prior compressors. SHRINK extracts data semantics in the form of linear segments to construct a compact knowledge base, using a dynamic error threshold that it adapts to data characteristics. Then, it captures the remaining data details as residuals to support lossy compression at diverse resolutions as well as lossless compression. As SHRINK identifies repeated semantics, its compression ratio increases with data size. Our experimental evaluation demonstrates that SHRINK outperforms state-of-art methods with an up to threefold improvement in compression ratio. |
| title | SHRINK: Data Compression by Semantic Extraction and Residuals Encoding |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.06713 |