Service Composition Framework for Big Data Service

Nam, Taewoo; Choi, Kyungsuk; Ok, Cheolmin; Yeom, Keunhyuk

doi:10.1109/ficloud.2014.58

Cited by 3 publications

(2 citation statements)

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“…With the development of the Internet, wireless communications and the widespread growth of the mobile devices, an exponential growth of data volume is present. For this reason, a modern systematized solution that intends to achieve value from the analysis of data should consider, not only traditional data sources (such as transactional databases or spreadsheets), but also data from electronic devices such as sensors, cameras, scanners, and devices capable to control other things (the Internet of Things: Nam, Choi, Ok, & Yeom, 2014).…”

Section: A New Types Of Datamentioning

confidence: 99%

“…Nuevos tipos de datos Con el desarrollo de Internet, las comunicaciones inalámbricas y la masificación de dispositivos móviles, se presenta un crecimiento exponencial del volumen de datos y con ello la aparición de nuevas fuentes y tipos de datos. Hoy en día una solución sistematizada que pretenda sacar valor a partir del análisis de los datos debe considerar, no solo las fuentes de datos tradicionales, como bases de datos transaccionales y hojas de cálculo, sino también datos originados por aparatos electrónicos como sensores, cámaras, escáneres y, en general, todo dispositivo que permita controlar las cosas -Internet de las cosas- (Nam, Choi, Ok, & Yeom, 2014).…”

Section: Desafíos En El Tratamiento Y Almacenamiento De Grandes Volúm...unclassified

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A model of multilayer tiered architecture for big data

Salinas

Lemus

2016

sist.telemat.

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Until recently, the issue of analytical data was related to Data Warehouse, but due to the necessity of analyzing new types of unstructured data, both repetitive and non-repetitive, Big Data arises. Although this subject has been widely studied, there is not available a reference architecture for Big Data systems involved with the processing of large volumes of raw data, aggregated and non-aggregated. There are not complete proposals for managing the lifecycle of data or standardized terminology, even less a methodology supporting the design and development of that architecture. There are architectures in small-scale, industrial and product-oriented, which limit their scope to solutions for a company or group of companies, focused on technology but omitting the functionality. This paper explores the requirements for the formulation of an architectural model that supports the analysis and management of data: structured, repetitive and non-repetitive unstructured; there are some architectural proposals –industrial or technological type– to propose a logical model of multi-layered tiered architecture, which aims to respond to the requirements covering both Data Warehouse and Big Data.

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Section: A New Types Of Datamentioning

confidence: 99%

Section: Desafíos En El Tratamiento Y Almacenamiento De Grandes Volúm...unclassified