scholarly journals Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems

2015 ◽  
Vol 2 (4) ◽  
pp. 166-186 ◽  
Author(s):  
Pekka Pääkkönen ◽  
Daniel Pakkala
Author(s):  
Janusz Bobulski ◽  
Mariusz Kubanek

Big Data in medicine contains conceivably fast processing of large data volumes, alike new and old in perseverance associate the diagnosis and treatment of patients’ diseases. Backing systems for that kind activities may include pre-programmed rules based on data obtained from the medical interview, and automatic analysis of test diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a computer data processing system using artificial intelligence to analyse and process medical images. We conducted research that confirms the need to use GPUs in Big Data systems that process medical images. The use of this type of processor increases system performance.


Author(s):  
Todor Ivanov ◽  
Sead Izberovic ◽  
Nikolaos Korfiatis

This chapter introduces the concept of heterogeneity as a perspective in the architecture of big data systems targeted to both vertical and generic workloads and discusses how this can be linked with the existing Hadoop ecosystem (as of 2015). The case of the cost factor of a big data solution and its characteristics can influence its architectural patterns and capabilities and as such an extended model based on the 3V paradigm is introduced (Extended 3V). This is examined on a hierarchical set of four layers (Hardware, Management, Platform and Application). A list of components is provided on each layer as well as a classification of their role in a big data solution.


2017 ◽  
Vol 90 ◽  
pp. 75-92 ◽  
Author(s):  
Sergi Nadal ◽  
Victor Herrero ◽  
Oscar Romero ◽  
Alberto Abelló ◽  
Xavier Franch ◽  
...  

2016 ◽  
Vol 14 (37) ◽  
pp. 23-44
Author(s):  
Sonia Ordóñez Salinas ◽  
Alba Consuelo Nieto Lemus

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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