A Survey on MapReduce Implementations

2016 ◽  
Vol 6 (1) ◽  
pp. 59-87 ◽  
Author(s):  
Amer Al-Badarneh ◽  
Amr Mohammad ◽  
Salah Harb
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Data Sets ◽  
Mapreduce Framework ◽  
Large Scale Data ◽  
Parallel Data ◽  
Efficiency Performance ◽  
Scale Data ◽  
Large Scale Data Sets ◽  
Open Issues

A distinguished successful platform for parallel data processing MapReduce is attracting a significant momentum from both academia and industry as the volume of data to capture, transform, and analyse grows rapidly. Although MapReduce is used in many applications to analyse large scale data sets, there is still a lot of debate among scientists and researchers on its efficiency, performance, and usability to support more classes of applications. This survey presents a comprehensive review of various implementations of MapReduce framework. Initially the authors give an overview of MapReduce programming model. They then present a broad description of various technical aspects of the most successful implementations of MapReduce framework reported in the literature and discuss their main strengths and weaknesses. Finally, the authors conclude by introducing a comparison between MapReduce implementations and discuss open issues and challenges on enhancing MapReduce.

scholarly journals Simplified Mapreduce Mechanism for Large Scale Data Processing

2018 ◽  
Vol 7 (3.8) ◽  
pp. 16
Author(s):  
Md Tahsir Ahmed Munna ◽  
Shaikh Muhammad Allayear ◽  
Mirza Mohtashim Alam ◽  
Sheikh Shah Mohammad Motiur Rahman ◽  
Md Samadur Rahman ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Processing Time ◽  
Programming Model ◽  
Data Sets ◽  
Hadoop Mapreduce ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Scale Data ◽  
Large Scale Data Sets

MapReduce has become a popular programming model for processing and running large-scale data sets with a parallel, distributed paradigm on a cluster. Hadoop MapReduce is needed especially for large scale data like big data processing. In this paper, we work to modify the Hadoop MapReduce Algorithm and implement it to reduce processing time.  

Artificial Neural Network Models for Large-Scale Data

2019 ◽  
pp. 406-439
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Data Sets ◽  
Neural Network Models ◽  
Large Scale Data ◽  
The World ◽  
Commercial Applications ◽  
Artificial Neural Network Models ◽  
Scale Data ◽  
Large Scale Data Sets

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.

Collection, Storage, Protection, and Sharing Issues With Large-Scale Data Sets

2017 ◽  
Author(s):  
Shirley M. Matteson ◽  
Sonya E. Sherrod ◽  
Sevket Ceyhun Cetin
Keyword(s):  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Scale Data ◽  
Large Scale Data Sets
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