http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/36 2026-10-06T02:58:38Z http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/636 Ingeniería de Software Basada en Búsqueda en Líneas de Productos de Software Trujillo Tzanahua, Guadalupe Isaura; Juárez Martínez, Ulises; Cortés Verdín, Karen Currently, software construction is moving towards industrialization by replacing the custom form of development with the use of approaches such as Software Product Lines (SPL) and Multiple Software Product Lines (MPL). These paradigms establish a common means of production to generate a variety of products through the reuse of inputs and automation of processes and thus meet the needs and requirements of the market instead of targeting specific customers. However, the management of multiple SPLs or MPLs is a challenge because the variant number of possible software products to be obtain expressed in the feature models is often large due to feature combinations. For this reason, it is not feasible to configure, deploy, or test all possible product variants. To support this decision-making process, this chapter investigates and applies a variant of the knapsack problem. Specifically, product configuration in an MPL is formulated as a Multiple-Choice Multi-dimensional Knapsack Problem (MMKP) and is solved with a Search-Based Software Engineering (SBSE) technique. First, a review of search and optimization techniques is provided to offer practical application elements for the development of software products based on SPL and Search-Based Software Engineering (SBSE). A genetic algorithm is then implemented in Python to solve the problem of configuring products in MPL. 2020-12-01T00:00:00Z http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/590 Horizontal Fragmentation of Data Warehouses Using Decision Trees Rodríguez-Mazahua, Nidia; Rodríguez-Mazahua, Lisbeth; López-Chau, Asdrúbal; Alor-Hernández, Giner One of the main problems faced by Data Warehouse (DW) designers is fragmentation. Several studies have proposed data mining-based horizontal fragmentation methods, which focus on optimizing the query response time and execution cost to make the DW more efficient. However, to the best of our knowledge there not exist a horizontal fragmentation technique that uses a decision tree to carry out fragmentation. Given the importance of decision trees in classification, since they allow obtaining pure partitions (subsets of tuples) in a data set using measures such as Information Gain, Gain Ratio and the Gini Index, the aim of this work is to use decision trees in the DW fragmentation. For this, the requirements necessary to carry out horizontal fragmentation using decision trees will be determined, and the fragmentation method will be designed, which will consist of determining the most frequent OLAP (On-line Analytical Processing) queries, analyzing the predicates used by the queries, and based on this build the decision tree, from which the horizontal fragments will be generated. The method will be implemented and validated using a case study in tourism. One of the main problems faced by Data Warehouse (DW) designers is fragmentation. Several studies have proposed data mining-based horizontal fragmentation methods, which focus on optimizing the query response time and execution cost to make the DW more efficient. However, to the best of our knowledge there not exist a horizontal fragmentation technique that uses a decision tree to carry out fragmentation. Given the importance of decision trees in classification, since they allow obtaining pure partitions (subsets of tuples) in a data set using measures such as Information Gain, Gain Ratio and the Gini Index, the aim of this work is to use decision trees in the DW fragmentation. For this, the requirements necessary to carry out horizontal fragmentation using decision trees will be determined, and the fragmentation method will be designed, which will consist of determining the most frequent OLAP (On-line Analytical Processing) queries, analyzing the predicates used by the queries, and based on this build the decision tree, from which the horizontal fragments will be generated. The method will be implemented and validated using a case study in tourism. 2020-08-01T00:00:00Z