Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression

• URL: http://arxiv.org/abs/2503.13246v1
• Date: Mon, 17 Mar 2025 14:58:22 GMT
• Title: Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression
• Authors: Guoyou Sun, Panagiotis Karras, Qi Zhang,
• Abstract summary: We propose a novel method for direct analytics on time series data compressed by the SHRINK compression algorithm.<n>Our approach offers reliable, high-speed outlier detection analytics for diverse IoT applications.
• Score: 15.122371541057339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic communication has emerged as a promising paradigm to tackle the challenges of massive growing data traffic and sustainable data communication. It shifts the focus from data fidelity to goal-oriented or task-oriented semantic transmission. While deep learning-based methods are commonly used for semantic encoding and decoding, they struggle with the sequential nature of time series data and high computation cost, particularly in resource-constrained IoT environments. Data compression plays a crucial role in reducing transmission and storage costs, yet traditional data compression methods fall short of the demands of goal-oriented communication systems. In this paper, we propose a novel method for direct analytics on time series data compressed by the SHRINK compression algorithm. Through experimentation using outlier detection as a case study, we show that our method outperforms baselines running on uncompressed data in multiple cases, with merely 1% difference in the worst case. Additionally, it achieves four times lower runtime on average and accesses approximately 10% of the data volume, which enables edge analytics with limited storage and computation power. These results demonstrate that our approach offers reliable, high-speed outlier detection analytics for diverse IoT applications while extracting semantics from time-series data, achieving high compression, and reducing data transmission.

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