# Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses
## 2 State of the Art of Horizontal Fragmentation Methods
The corresponding optimization problem involves calculating a fact table partitioning by limiting the number of fragments by a constant B (quantity of fragments established by the DW administrator) and minimizing the number of I/O operations. A hill-climbing method was developed to choose a near-optimal solution for selecting an optimal fragmentation scheme in a relational DW. The hill-climbing algorithm has two steps: (1) it finds an initial HFS using an affinity algorithm, and (2) it iteratively improves the initial solution to minimize query execution cost and satisfy B. Likewise, the problem of selecting an HFS that is supported by the ant-colony-based approach was modeled in [20]; the variables were: the non-fragmented DW scheme, the workload commonly executed, and the largest quantity of partitions necessary for the DW administrator. The HFS that minimizes the total cost of request loading is the resulting output. The approach taken was based on cost. In [9; 21], all the DW tables were fragmented into several buckets (NB) using a hash-partitioning approach and then they were distributed evenly among the cluster nodes to optimize star join queries. The buckets were built in three stages: (1) finding out the near-best NB and the partitioning key by employing the balanced k-means algorithm; (2) obtaining the fact table buckets; and (3) building dimension buckets. [PERSON] and [PERSON] [22] implemented an assisted incremental horizontal fragmentation technique with temporary materialized views by a web service. This approach has its basis in managing temporary materialized views for optimizing frequent new queries before proceeding with the fragmentation implementation. The authors used a web service to automatically monitor and improve DW performance and reduce both manual efforts and implementation costs. The web service selects and implements the HFS, and then monitors the performance of the DW to avoid any degradation. However, to adjust decision-making systems with big data management, in [23], the authors proposed the merger of NoSQL graph-oriented data into the DW; the work details a new method named Big Parallel-ETL which has the aim of adjusting the traditional ETL process (extract-transform-load) through big data tools to speed up data manipulation with the basis of the well-known MapReduce concept distinguished by its suitable parallel-processing feature. The system architecture is comprised of two main layers; one of them has the data sources of the system (Neo4j) and the other layer is comprised of the ETL process that works with the MapReduce paradigm. The main idea of the work consists of operating on a JSON file containing Neo4j data mixed with DW metadata to arrange the multidimensional schema utilizing the MapReduce paradigm beginning with the horizontal partitioning operation and then transforming the sub-JSON files into column-oriented tables to obtain the corresponding multidimensional layout to be unified into the target DW. Likewise, [PERSON] et al. [24] proposed an allocation approach of \"objects\" to \"storages\" where the nature of the former is defined by the subject area. Later, they considered the speed up of the join operation while processing big data in a specific area. To decrease the execution time of this activity, it is required to optimally distribute the operand tables. Therefore, parallel implementation of the join execution needs an equal data distribution among the DW processors in the cluster. To efficiently resolve the optimal big data distribution, the authors formalized a heuristic method with polynomial computational complexity presenting the objective function and the system of restrictions. The study of the established technique considering different data bulks and diverse data warehouses was realized. A report of the experiments to validate the heuristic greedy optimal distribution proposal was given.