Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing

One of the most important stages of Computerized Adaptive Testing is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this paper, it is proposed the integration of Association Rule Mining as an item selection criterion in a CAT system. We present the analysis of association rule mining algorithms such as Apriori, FP-Growth, PredictiveApriori and Tertius into two data set with the purpose of knowing the advantages and disadvantages of each algorithm and choose the most suitable. We compare the algorithms considering number of rules discovered, average support and confidence, and velocity. According to the experiments, Apriori found rules with greater confidence, support, in less time.Una de las etapas más importantes de las pruebas adaptativas informatizadas es la selección de ítems, en la cual se utilizan diversos métodos que presentan ciertas debilidades al momento de su aplicación. Así, en este trabajo, se propone la integración de la minería de reglas de asociación como criterio de selección de ítems en un sistema CAT. Se presenta el análisis de algoritmos de minería de reglas de asociación como Apriori, FP-Growth, PredictiveApriori y Tertius en dos conjuntos de datos con el fin de conocer las ventajas y desventajas de cada algoritmo y elegir el más adecuado. Se compararon los algoritmos teniendo en cuenta el número de reglas descubiertas, el soporte y confianza promedios y la velocidad. Según los experimentos, Apriori encontró reglas con mayor confianza y soporte en un menor tiempo

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables

Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method

Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method

Application of Dynamic Fragmentation Methods in Multimedia Databases: A Review

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented

Smart Technologies. SmartTech-IC 2022: Third International Conference on Smart Technologies, Systems and Applications

La Universidad Politécnica Salesiana ha estado promoviendo la investigación científica proporcionando financiamiento para el desarrollo y ejecución de propuestas en diversas áreas de investigación. En la Sede Cuenca, se han formado grupos multidisciplinarios para llevar a cabo estas propuestas de investigación. Aunque estos indicadores demuestran resultados favorables en la implementación de una cultura de investigación sólida, todavía se informan proyectos que, debido a varios factores, no logran publicar sus resultados en revistas indexadas. Entre estos factores se encuentran los altos costos de movilidad de los investigadores para presentar sus trabajos en eventos indexados, así como la falta de implementación de criterios adecuados de cienciometría. Esto ha impedido que, en muchos casos, investigaciones con resultados sobresalientes no sean comunicadas a la comunidad científica internacional, lo que no contribuye al aumento de la productividad académica institucional. Además, en algunos casos, se publican trabajos en revistas sin una indexación que contribuya a los indicadores institucionales (Castillo y Powell, 2019) (Guerrero-Casado, 2017)