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

arXiv: https://arxiv.org/abs/2503.13246 · HTML: https://mlsanity.com/paper/2503.13246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data](https://mlsanity.com/paper/2205.00271/) (2022)
- [Goal-Oriented Communications for the IoT and Application to Data Compression](https://mlsanity.com/paper/2211.05378/) (2022)
- [Plato: Approximate Analytics over Compressed Time Series with Tight Deterministic Error Guarantees](https://mlsanity.com/paper/1808.04876/) (2018)
- [SHRINK: Data Compression by Semantic Extraction and Residuals Encoding](https://mlsanity.com/paper/2410.06713/) (2024)

## Abstract (arXiv)

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.