Parallel Algorithm for Reduction of Data Processing Time in Big Data

• Autores: Silva, Jesús, Hernández Palma, Hugo, Niebles Núẽz, William, Ovallos-Gazabon, David, Varela, Noel
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/652134
• Enlace del recurso: http://hdl.handle.net/10757/652134
• Nivel de acceso: acceso abierto
• Materia: Computer architecture; Data mining; Large dataset; Data size; Large datasets; Large volumes; Parallel version; Technological advances

Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.