# Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses
## 2 State of the Art of Horizontal Fragmentation Methods  
In the literature, some algorithms have been developed to address the challenge of choosing a horizontal fragmentation scheme from a given DW. They are classified as minterm generation algorithms [1], affinity-based algorithms, cost-model-driven algorithms, and data-mining-based algorithms, to mention a few [11]. This section presents some related horizontal fragmentation (HF) techniques and their main characteristics. The approach proposed in [12] presented a method that uses linear programming to resolve the NP-hard problem of finding an HF scheme in a relational DW. The problem was determined in two concurrent objectives, named: the I/O amount required to answer the queries, and the number of partitions produced to recognize the optimal solutions matched to the Pareto dominance concept. According to [13], although many techniques have been proposed to speed up data access in the DW, they cannot be applied directly to the object-relational DW (ORDW), since it involves abstract data types for complex data stored in an object format. Therefore, the authors proposed a partition-based approach to efficiently access data in an ORDW. The approach has three steps: (1) obtaining minterm predicates, (2) partitioning the object-relational scheme by derived horizontal partitioning, and (3) fragment selection based on DW properties and query trends. In contrast, in [14], to obtain fewer fragments and improve the performance of OLAP queries, [PERSON] and [PERSON] fragmented horizontally just the fact table considering the selectivity of the predicates, their occurrence amount, and their access frequencies. They proposed to split only the fact relation without using the primary HF of the dimension relations. Later, in [15], the authors introduced an improved version of their approach presented in [14]. A technique established on frequent itemset mining was proposed in [16] towards partition, bucket, and sort tables (PBSTs) into a big DW with the more common predicate attributes in the workload. This method considered the quantity of the relation attributes, data skew, and the physical features of the cluster nodes. The authors employed a hash-partitioning approach, which horizontally splits all the tables of a big relational DW, utilizing workload-based PBST methods. [PERSON] et al. [17] focused on the \"Dist-Key\" which is a horizontal partitioning type widely used in DW systems on the market. This kind of horizontal partitioning divides the tuples of a table by using a hash function on the values of a particular attribute identified as the distribution key. Consequently, the proposed strategy tries to reduce the query network cost by choosing the optimal attribute to hash-distribute every DW relation. They suggested BaW (best of all worlds), a mixed method that merges heuristic and exact techniques to select the best distribution key in favor of some tables. Nevertheless, [PERSON] and [PERSON] [18] developed a technique called Chabok that involves a two-phase Map-Reduce to resolve DW issues incorporating big data. Chabok is utilized in repositories with the star schema and allows the calculation of distributive measures. This approach deals efficiently with big dimensions; in these dimensions, data size is larger than the bulk of a node. Chabok partitions horizontally the fact table. The Fact Mapper nodes store an identical quantity of tuples in the case of similar nodes. Hadoop and the TPC-DS benchmark were utilized to implement Chabok. The query response time on Chabok overcame significant big data tools for data warehousing, such as Hive and Spark-SQL. Another approach based on affinity is [19], where the authors considered the horizontal fragmentation of the DW through a simplified decision problem that takes into account the fragmentation derived from the fact table according to the partitioning scheme of a dimension table making use of solely one attribute.