# Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses
## 5 Conclusions and Future Work  
Technological advances have made it possible to collect large amounts of data in different fields; therefore, there is a constant need to develop tools that help the process of extracting precise information from the enormous volume of data that are converted into knowledge. The DWs have proven to be efficient for the analysis of these massive data; for this reason, they are an excellent option to be implemented in the monitoring of tourist activity, in which, to stay at a competitive level, it is necessary to know the client preferences. As mentioned throughout this article, DW fragmentation achieves optimized response time and execution costs of OLAP queries. Several techniques for both vertical and horizontal fragmentation have been developed; however, to the best of our knowledge, a decision tree has not been used to fragment the DW, which represents an unexplored area within DW research. In this work, the classification capability of decision trees is exploited to use them in DW horizontal fragmentation. A horizontal fragmentation method, called FTree, has been developed that uses decision trees to obtain fragmentation schemes that achieve the optimization of OLAP queries in a DW. FTree was applied in a tourist DW, taking advantage of a large amount of data available in this area. The results will benefit both designers and users of data warehouses as well as researchers from the computational area.  
In the future, we will extend FTree to provide dynamic fragmentation of data warehouses, monitoring the access patterns of the DW to detect when to modify the horizontal fragmentation scheme to avoid the reduction in query performance. Additionally, the fragmentation of data lakes will be considered.  
Conceptualization, L.R.-M., N.R.-M. and A.L.-C.; formal analysis, N.R.-M.; investigation, N.R.-M. and L.R.-M.; methodology, [PERSON]-M., [PERSON] and [PERSON]-[PERSON]; data curation, [PERSON]-M.; visualization, [PERSON]-M.; funding acquisition, [PERSON]-M. and [PERSON]-H.; resources, [PERSON]-M. and [PERSON]-C.; writing--original draft, [PERSON]-M.; writing--review & editing, [PERSON]-M., [PERSON]-H., [PERSON]C. and [PERSON]-H.; project administration, [PERSON]-M., [PERSON]-C. and [PERSON]-C.; supervision, [PERSON]-[PERSON] and [PERSON]-C.; validation, [PERSON]-M. All authors have read and agreed to the published version of the manuscript.  
The National Council of Science and Technology (CONACYT) as well as the Public Secretariat of Education (SEP) financed this project through the Sectorial Fund of Research for Education, grant number A1-S-51808.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Query** & \multicolumn{2}{c}{**FTree**} & \multicolumn{4}{c}{**Kechar \& Nait-Bahloul [14]**} \\  & **ITC** & **RTC** & **Cost** & **ITC** & **RTC** & **Cost** \\ \hline \(q_{1}\) & 4200 & 0 & 4200 & 3500 & 0 & **3500** \\ \(q_{2}\) & 975 & 0 & **975** & 2475 & 0 & 2475 \\ \(q_{3}\) & 0 & 0 & **0** & 2900 & 0 & 2900 \\ \(q_{4}\) & 450 & 0 & **450** & 1950 & 0 & 1950 \\ \(q_{5}\) & 0 & 0 & **0** & 2175 & 0 & 2175 \\ \(q_{6}\) & 1500 & 0 & 1500 & 1500 & 0 & 1500 \\ \(q\) & 450 & 0 & **450** & 1950 & 0 & 1950 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Comparison of query costs for the two fragmentation schemes.  
Data Availability Statement: The data presented in this study is available on request from the corresponding author.  
The authors thank the National Technological of Mexico (TecNM) for supporting this research. In addition, the National Council of Science and Technology (CONACYT) and the Public Secretariat of Education (SEP) patronized this project.  
The authors declare no conflict of interest.