# Text Semantics-Driven Data Classification Storage Optimization
## 1 Introduction  
The exponential growth of data in recent times has given rise to a significant conundrum concerning the balance between storage performance and costs. In addressing this challenge, hybrid storage solutions have emerged as a promising compromise for consumers, adept at striking a delicate equilibrium among factors such as energy efficiency, cost-effectiveness, and performance optimization [1; 2; 3; 4; 5]. Recent statistical data underscore the urgency of this matter, revealing that 1.5% of world energy consumption is contributed by data centers, with storage systems accounting for a substantial 40% of this total [6]. The imperative to curtail energy consumption and operating costs has become a paramount concern for storage systems. Data classification storage is proven to save storage energy consumption and costs. The existing literature features extensive research on the identification of hot data [7; 8; 9; 10] and the optimization of data placement strategies [11; 12]. Hot data identification involves intricate analyses of factors like access frequency, recency, and other relevant features. Data placement strategies [13; 14; 15; 16] involve both horizontal placement, addressing the distribution of data within a storage layer, and vertical placement, managing the distribution of data across various storage layers. These strategies aim to enhance the efficient organization of data. It is worth noting that most current classification methods primarily rely on access frequency for data. However, as the scale of the data increases, the cost associated with collecting frequency information over time becomes non-negligible. Moreover, in the context of the massive data era characterized by continuous data growth, diverse data types, and intricate user access patterns, classification methods that solely hinge on accessfrequency gradually lose their efficacy. In response to these challenges, this paper advocates for a more holistic approach to data classification storage. Unlike traditional data classification storage methods, which rely solely on access frequency, we propose an effective data classification storage method using text semantic similarity to extract seasonal features. First, we adopt an approach based on text semantic similarity to extract data textual seasonal features for classification. More specifically, we realize data classification from coarse to fine by setting two text semantic similarity thresholds in a dual-layer strategy. In the first layer, by setting a low threshold, we ensure that the filtered data are all without seasonal features. In the second layer, by setting a high threshold, the data without seasonal features are further screened and the category of seasonal feature data is determined. Second, we propose a framework for data classification storage based on text seasonal features. We classify the data into \"hot data\" (i.e., data with current seasonal characteristics) and cold data (i.e., data with other seasonal characteristics) based on its seasonal characteristics. We achieve classified storage of the data by placing the data on different performance disks. This storage framework uses text semantic similarity as the basis for data classification, filling the gap of relying solely on access frequency to partition data. At the same time, this method achieves regular adjustment of the storage system with seasonal changes by mining the seasonal feature attributes contained in the semantics of data texts. The experimental validation of our method demonstrates its superiority in achieving not only reduced energy consumption but also cost-effectiveness in comparison to conventional methods, thus offering a forward-looking solution for the evolving demands of data storage systems. In brief, this paper makes the following contributions:  
* We explore a dual-layer strategy for mining seasonal features of data texts to classify them based on text semantic similarity. * We propose a cost-effective data classification storage framework based on text seasonal features.