# Text Semantics-Driven Data Classification Storage Optimization
## 3 The Data Classification Storage Framework Based on Season
### Data Storage  
Analyzing historical data, we have identified potential seasonal patterns in data access. These patterns are inherently tied to time, wherein the data exhibit recurring patterns or fluctuations during specific seasonal intervals. These observed seasonal data access laws provide the basis for categorizing the data into \"hot\" and \"cold\" data, based on their seasonal features. In this context, data with the current seasonal feature are considered \"hot\" data, while data with other seasonal features are classified as \"cold\" data. We implement data classification and storage by allocating high-performance disks for hot data and utilizing low-performing disks for cold data. As the seasons change, the disk state can be transformed and the data can also be migrated. Algorithm 2 describes the whole process of data classification storage and migration. The primary objective of the algorithm is to assign the data to disks based on their performance, segregating the data into disks corresponding to different temperature levels. We do not care about the data migration overhead. This is because seasonal changes are a necessary condition for triggering data migration, and there are only three seasonal changes in an entire year. That is to say, data migration only occurs three times in one year. Therefore, the overhead resulting from data migration during their lifespan after data placement can be disregarded when compared to the data storage overheads post-data placement. Reducing the overall energy consumption and cost of the storage system is facilitated with this method, all the while ensuring that the quality of user access remains intact.  
```
Input: \(c_{i}\): the class sets \(c_{i}\)=\(\{c_{spring},c_{summer},c_{autumn},c_{winter},c_{other}\}\) \(E\): the number of data texts \(n\): the number of classes \(m\): the current month \(Season(m)\): determine the season of the current month Output: The data storage area according to hot/cold status
1 fort=0 to E-1 do
2 forg=0 to n-1 do
3 determine the data class of \(c\)
4 end for
5
6 end for
7 whiletruedo switch\(Season(m)\)do
8 casespringdo
9 the data of \(c_{spring},c_{other}\) are hot data;
10 the data of \(c_{summer},c_{autumn},c_{winter}\), are cold data;
11 migrate hot data to high-performance HDD and cold data to low-performance HDD;
12 end for
13 casesummerdo
14 the data of \(c_{summer},c_{other}\) are hot data;
15 the data of \(c_{spring},c_{autumn},c_{winter}\) are cold data;
16 migrate hot data to high-performance HDD and cold data to low-performance HDD;
17
18 end for
19 caseautumndo
20 the data of \(c_{autumn},c_{other}\) are hot data;
21 the data of \(c_{spring},c_{summer},c_{winter}\) are cold data;
22 migrate hot data to high-performance HDD and cold data to low-performance HDD;
23 end for
24 casewinterdo
25 the data of \(c_{winter},c_{other}\) are hot data;
26 the data of \(c_{spring},c_{summer},c_{autumn}\) are cold data;
27 migrate hot data to high-performance HDD and cold data to low-performance HDD;
28 end for
29 end for
30
31 end for
32
33 end for
```  
**Algorithm 2**Data Classification Storage and Migration  
The data storage area according to hot/cold status  
\(t\)=0 to E-1 do
3 forg=0 to n-1 do
4 determine the data class of \(c\)