Sublinear Algorithms for Big Data Applications

The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data a...

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Detalles Bibliográficos
Autor principal: Wang, Dan (-)
Autor Corporativo: SpringerLink (Online service) (-)
Otros Autores: Han, Zhu
Formato: Libro electrónico
Idioma:Inglés
Publicado: Cham : Springer International Publishing 2015.
Colección:SpringerBriefs in Computer Science.
Springer eBooks.
Acceso en línea:Conectar con la versión electrónica
Ver en Universidad de Navarra:https://innopac.unav.es/record=b30634106*spi
Descripción
Sumario:The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.
Descripción Física:XI, 85 p. : 30 il., 20 il. col
ISBN:9783319204482