https://hdl.handle.net/10481/33254 2026-04-06T14:47:27Z 2026-04-06T14:47:27Z Luengo Martín, Julián García Gil, Diego Jesús Ramírez-Gallego, Sergio García López, Salvador Herrera Triguero, Francisco https://hdl.handle.net/10481/99405 2025-01-16T11:22:55Z

Big data preprocessing: enabling smart data Luengo Martín, Julián; García Gil, Diego Jesús; Ramírez-Gallego, Sergio; García López, Salvador; Herrera Triguero, Francisco The massive growth in the scale of data has been observed in recent years, being a key factor of the Big Data scenario. Big Data can be defined as high volume, velocity, and variety of data that require a new high-performance processing. Addressing Big Data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. Being a very common scenario in real-life applications, the interest of researchers and practitioners on the topic has grown significantly during these years. Among Big Data disciplines, data mining is a key topic, enabling the user to extract knowledge from enormous amounts of raw data. However, this raw data is not always in the best condition to be treated, analyzed, and surveyed. The application of preprocessing techniques is a must in real-world applications, to ensure quality data, Smart Data, for a proper treatment and analysis. The term Smart Data refers to the challenge of transforming raw data into quality data that can be appropriately exploited to obtain valuable insights. This book aims at offering a general and comprehensible overview of data preprocessing in Big Data, enabling Smart Data. It contains a comprehensive description of the topic and focuses on its main features and the most relevant proposed solutions. Additionally, it considers the different scenarios in Big Data for which the application of data preprocessing techniques can suppose a real challenge. Data preprocessing is a multifaceted discipline that includes data preparation, compounded by integration, cleaning, normalization, and transformation of data; data reduction tasks such as feature selection, instance selection, and discretization; and resampling techniques to deal with imbalanced data. This book stresses the gap with standard data preprocessing techniques and their Big Data equivalents, showing the challenging difficulties in their development for the latter. It also covers the different approaches that have been traditionally applied and the latest proposals in Big Data preprocessing. Specifically, it reviews data reduction methods, imperfect data approaches, discretization techniques, and imbalanced data preprocessing solutions. Finally, this book describes the most popular Big Data libraries for machine learning, focusing on their data preprocessing algorithms and utilities.

Montes, Rosana Zuheros, Cristina Morales, Jeovani M. Zermeño, Noe Duran, Jerónimo Herrera Triguero, Francisco https://hdl.handle.net/10481/88032 2024-02-02T12:48:02Z

Design and consensus content validity of the questionnaire for b-learning education: A 2-Tuple Fuzzy Linguistic Delphi based Decision Support Tool Montes, Rosana; Zuheros, Cristina; Morales, Jeovani M.; Zermeño, Noe; Duran, Jerónimo; Herrera Triguero, Francisco Este trabajo presenta un modelo lingüístico de toma de decisión que aporta además una solución software para poder aplicar la validación de constructo mediante juicio de expertos totalmente online y con asistencia al cálculo del consenso y a la toma de decisión en cuanto a los cambios que se debe tomar para mejorar de forma iterativa un instrumento de toma de datos. La idea clave es validar el cuestionario definiendo la validez de cada ítem como un problema de Toma de Decisiones. Tomando la opinión de expertos, medimos el grado de consenso, el grado de consistencia y la puntuación lingüística de cada ítem, con el fin de detectar aquellos ítems que afectan, positiva o negativamente, a la calidad del instrumento. El trabajo aborda un caso real de validación de instrumentos. Para ello se ocupa de la necesidad en contextos educativos de evaluar una experiencia de blended learning con un cuestionario consensuado que abarca dimensiones relativas a Flipped Classroom y Mobile Learning. Además, contribuimos a este problema de consenso desarrollando una herramienta online bajo licencia GPL v3. El software visualiza las valoraciones colectivas para cada iteración y ayuda a determinar qué partes del cuestionario deben modificarse para alcanzar una solución consensuada.; This work presents a linguistic decision-making model that also provides a software solution for applying construct validation by means of fully online expert judgment and with assistance in calculating consensus and making decisions regarding the changes to be made to iteratively improve a data collection instrument. The key idea is to validate the questionnaire by defining the validity of each item as a Decision Making problem. Taking expert opinion, we measure the degree of consensus, the degree of consistency and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. The model deals with a real case of instrument validation. It deals with the need in educational contexts to evaluate a blended learning experience with a consensual questionnaire that covers dimensions related to Flipped Classroom and Mobile Learning. In addition, we contribute to this consensus problem by developing an online tool under GPL v3 license. The software visualizes the collective ratings for each iteration and helps to determine which parts of the questionnaire should be modified to reach a consensus solution. This is an open access article under the CC BY-NC-ND license