## 2 Literature Review
### Machine Learning-Based Techniques  
Machine learning models have been widely adapted in recent years to overcome various challenges related to data fragmentation. Such an approach takes the benefits of clustering, heuristic, and correlation algorithms to analyze databases and enhance the performance of their processing. The authors of ref. [5] proposed a clustering-based fragmentation technique where table attributes with similarity are placed in the same fragment cluster to improve the efficiency and effectiveness of vertical fragmentation in DDBMS. This technique aimed to create more significant and efficient vertical fragments by considering the frequency of user queries and Euclidean distances between attributes. However, heuristic solutions may not always guarantee optimal results and could potentially introduce suboptimal fragmentation or allocation decisions. The authors of ref. [6] presented a random forest algorithm for feature selection to remove irrelevant or correlated attributes from the dataset. The method aimed to improve query processing time by reducing the attribute size and creating more efficient fragments. It effectively decreases the size of high-dimensional data by reducing the data dimensions without losing the related data. However, there are concerns regarding the system's ability to scale and adapt effectively. The authors of ref. [7] introduced a heuristic k-means approach for vertical fragmentation and allocation to address the challenges of communication costs and response time in DDBS. The proposed approach combines multiple techniques to create a promising solution and aims to achieve optimal vertical fragmentation and allocation by employing a heuristic k-means approach. The obtained results demonstrate its potential for improving DDBS performance. However, the process is time consuming and leads to complexity issues. The authors of ref. [8] proposed a Cluster-based Distributed and Parallel Database System (CB-DDBS) architecture for improving the performance and efficiency of distributed database systems (DDBS). CB-DDBS enables clients to access the clustered DDBS from anywhere while allowing for static decisions on vertical and horizontal fragmentation, allocation, and replication at the initial stage of the design. However, the method lacks the details of the data regarding potential scalability challenges, constraints in handling large-scale databases, and trade-offs between performance and resource utilization. The authors of ref. [9] proposed a heuristic clustering-based approach for vertical fragmentation and data allocation in order to improve the throughput of relational DDBS to address the challenge of reducing transmission costs (TC) in distributed database systems (DDBS). The method combines an aggregated similarity-based fragmentation procedure, effective site clustering, and a greedy algorithm-driven data allocation model. However, the approach lacks a way to evaluate its scalability and potential trade-offs between throughput improvement and leads to increased network overhead. The authors of ref. [10] presented an efficient set of query execution plans to achieve the optimization of complex queries in cloud computing environments. The study introduced a set of robust heuristic algorithms, including Branch-and-Bound, Genetic, Hill Climbing, and Hybrid Genetic-Hill Climbing, to find near-optimal query execution plans and maximize the benefits. However, the computational complexity of the heuristic algorithms may pose challenges when dealing with large-scale and complex query workloads. Moreover, the algorithms lack satisfactory scalability and adaptability to diverse cloud database environments. The authors of ref. [11] proposed a combination of data fragmentation and query generalization method that enables the system to efficiently distribute and process. The method uses clustering-based fragmentation and the Anti-Instantiation operator to derive semantic fragments and support intelligent flexible query answering.