# Text Semantics-Driven Data Classification Storage Optimization
## 2 Related Work and Motivation  
### Related Work  
We provide a concise overview of noteworthy studies in this field through data classification to achieve cost-effective storage [17; 18] in this section. SEA [11] is designed to enhance energy efficiency within RAID-structured storage systems. SEA employs popularity weights for data classification, storing them separately on different disks. However, it relies on simple statistics for popularity, potentially overlooking nuanced patterns in data access. Furthermore, the definition of popularity is essential, which may lead to suboptimal results in dynamic environments with changing data access patterns. GreenHDFS [19] achieves energy-proportional behavior in data centers. According to the energy consumption and performance, storage areas are categorized into two areas (hot and cold). Although GreenHDFS achieves 26% energy savings of a Hadoop cluster in the experiment, the reliance on statistical metrics for data classification may limit its adaptability to diverse workloads. [PERSON]'s [20] approach of configuring servers into two zones is efficient for energy conservation. However, putting servers in the cold zone to sleep raises concerns about responsiveness and latency. The trade-off between energy savings and potential delays in activating servers needs careful consideration, especially in environments with unpredictable access patterns. [PERSON] et al. [21] built a cost-effective model for distributed storage systems in which racks are classified into active and sleep zones. While the approach effectively reduces energy consumption, relying on statistical metrics for access regularity may oversimplify the dynamics of real-world data access patterns. Additionally, the real performance of sleeping servers in the sleep zone may vary based on workload characteristics. [PERSON] et al. [22] partitioned data into three classes according to the file characteristics. The rigid division of storage areas into reduplication, hot, and cold zones may adapt well to evolving data access patterns. While the approach shows energy savings under specific conditions, its effectiveness may diminish in dynamic environments. K-ear [23] categorizes data into multiple groups by extracting seasonal period characteristics using the K-means clustering algorithm. These categories are then stored in various regions within a cloud storage system. While considering seasonal features is a step forward, the K-means algorithm may limit the model's ability to capture complex relationships in the data. The shallow features extracted may not provide a comprehensive understanding of the data dynamics, particularly in scenarios with intricate access patterns. CSEA [4] also adopts access frequency as the basis for data classification. Unlike K-ear, it considers the climatic season characteristics of data access and applies a fine-grained data layout strategy from the perspective of climate and seasonal characteristics, reducing the storage system's expenses. We compare the related studies in Table 1, most of which only depend on access frequency. Our solution analyzes data from the perspective of text semantics and implements classification, providing a new idea for data classification storage. Motivation  
Indeed, much of the existing research in the realm of classification storage is rooted in the analysis of access frequency. Notably, within sectors such as banking and e-commerce, researchers have discovered periodic regularities in data access frequency, uncovering periodic hot-spot data by scrutinizing applications on enterprise-level storage servers. In our study, we investigate the inner link between data access frequency and seasonal cycle laws. We obtain the access frequency by searching for keywords on the Baidu Index website. The statistical period for the access frequency provided by the website is from 2011 to 2024. We intercept the data from 2017 to 2024 to display the seasonal characteristics corresponding to the access frequency.