# Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses  
[PERSON]  
1 Tecnologico Nacional de Mexico/I. T., Av. Oriente 9 852, Col. [PERSON],  
Orizaba C.P. 9420, Veracruz, Mexico Universidad Autonoma del Estado de Mexico, Centro Universitario UAEM Zumpango, Camino Viejo a Jilotzingo Continuacion Calle Rayon, Valle Hermoso, Zumpango C.P. 55600, Estado de Mexico, Mexico Universidad del Papaloupan, Calle Circuito Central #200, Col.  
principally composed of a considerably central table (fact table) that contains the volume of the data with no redundancy, and a group of smaller auxiliary tables (dimension tables), one for every single dimension [5]. The main features of data warehouses are (1) their data complexity because of the presence of hierarchy attributes, (2) a high amount of data, and (3) the query complexity due to the presence of joins and aggregations [6]. For a few decades, various fragmentation methods for DW have been presented since this technique has the advantage of minimizing OLAP (online analytical processing) query response time as well as having important benefits in the operations of table loading and maintenance [7]. DW fragmentation is more complex and challenging compared to object-oriented and relational database partitioning due to the variety of options for fragmenting a star schema. In a DW, either the dimension tables, the fact table, or both can be fragmented. Most of the proposed work focuses on horizontal fragmentation because fact tables typically have a high data volume, i.e., they are about millions of tuples [8], and queries usually only require a subset of them. Horizontal fragmentation typically divides DW tables into partitions of tuples according to the workload. The main advantages of decision trees are that their construction does not require domain expertise or configuration, they deal with multidimensional data, the steps of learning and classification involved in decision tree induction are uncomplicated and quick, and they achieve adequate performance. Furthermore, decision trees are important in classification because they permit obtaining pure partitions (tuple subsets) in a data set utilizing measures including the Gini index, information gain, and gain ratio. Most decision tree induction algorithms have a top-down approach; the decision tree is recursively built by dividing the training data into smaller subsets. The representation of knowledge learned in the shape of a tree is intuitive and usually easy for humans to assimilate; in addition, decision tree classifiers have good precision [9]. Until the time of analyzing the state of the art for this paper, the development of a horizontal fragmentation algorithm for data warehouses that uses decision trees has not been published. The use of a decision tree to horizontally fragment a relational database considering OLTP (online transaction processing) workloads has been reported in [10]; however, [10] does not take into account the characteristics of a DW such as the multidimensional model and OLAP queries. This article describes a horizontal fragmentation method, called FTree, which is focused on obtaining fragmentation schemes that decrease the OLAP query execution cost. To achieve this, first, a comprehensive analysis of the literature about existing horizontal fragmentation methods for DW was realized, and later, FTree was introduced. FTree begins selecting the relevant decision support queries by examining the predicates employed by the workload, and then according to these, it builds the decision tree to choose the horizontal fragmentation scheme. To verify the effectiveness of FTree, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was developed, and the fragmentation method was tested on this, achieving results that prove the benefit obtained by FTree.