Título del libro: Lecture Notes In Networks And Systems
Título del capítulo: Model for Managing Large Volumes of Data (MMLVD): An Approach Based on MapReduce and Machine Learning
HEBERTO FERREIRA MEDINA; SERGIO ROGELIO TINOCO MARTINEZ;
Autores externos:
Idioma:
Año de publicación:
2026
Palabras clave:
Aggregate pipeline; Data volume; Exponential growth; Large volumes; Machine-learning; Map-reduce; MongoDB; Storage modelling; Volume velocities; Work-flows
Resumen:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.The exponential growth of data poses challenges derived from the 5Vs of Big Data (Volume, Velocity, Variety, Veracity, and Value), which require advanced storage, processing, and analysis models. This study presents a meta-analysis of more than 100 publications indexed in Scopus, IEEE, and Springer on the use of MapReduce and machine learning (ML) in Big Data management, identifying trends, limitations, and opportunities. The results show critical shortcomings in traditional approaches: 1) high latency in iterative ML algorithms due to repeated disk operations in MapReduce/Hadoop; 2) lack of native interoperability between NoSQL databases (such as MongoDB) and ML pipelines, creating silos and costly migrations; 3) inefficient workflows that do not optimise the entire data cycle or adequately handle speed and variety in distributed environments. To overcome these limitations, MMLVD (Model for managing large volumes of data) is proposed, a hybrid framework that integrates: 1) MapReduce parallelism, 2) the predictive capabilities of machine learning, and 3) MongoDB?s native Aggregation Framework. MMLVD optimises everything from ingestion to predictive modelling, achieving higher performance, scalability, and cost reduction. It provides a practical and holistic solution applicable to healthcare, finance, e-commerce, and the Internet of Things (IoT), closing the gaps left open by current literature that relies on fragmented tools or partial evolutions such as Spark. This model advances the state-of-the-art through a workflow-centric architecture that natively combines distributed processing, flexible storage, and advanced predictive analytics.
Entidades citadas de la UNAM:
ISBN:
9783032248060
Editorial:
Springer Science and Business Media Deutschland GmbH
