Abstract：
The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage technology.
摘要：
数据量呈指数级增长，因此需要采用替代存储解决方案，DNA储存是最有前途的解决方案。然而,与合成和测序相关的高昂成本阻碍了其发展。预压缩数据被认为是降低存储成本的最有效方法之一。然而,不同的压缩方法对同一文件产生不同的压缩比，用单一方法压缩大量文件可能达不到最大压缩率。本研究提出了一种基于机器学习分类算法的多文件动态压缩方法，该方法为每个文件选择合适的压缩方法，以尽可能最大程度地减少存储到DNA中的数据量。首先,四种不同的压缩方法被应用于收集的文件。随后，选择最佳压缩方法作为标签，以及文件类型和大小用作功能，将其放入七种机器学习分类算法中进行训练。结果表明，在验证集和测试集上，k最近邻算法在大多数时间优于其他机器学习算法。准确率超过85%，波动性较小。此外,根据k-近邻模型可以实现30.85%的压缩率，与传统的单一压缩方法相比，超过4.5%，在0.48亿至30亿美元/TB的范围内节省了大量的DNA存储成本。与传统的压缩方法相比,多文件动态压缩方法在压缩多个文件时表现出更显著的压缩效果。因此,它可以大大降低DNA存储的成本，并促进DNA存储技术的广泛实施。
