Fault-tolerant technology for big data cluster in distributed flow processing system

2020 ◽  
Vol 18 (2) ◽  
pp. 101-110
Author(s):  
Zhicheng Jia
Keyword(s):  
Big Data ◽  
Fault Tolerant ◽  
Processing System

scholarly journals Analysis and Experimental Study of HDFS Performance

TEM Journal ◽  
2021 ◽  
pp. 806-814
Author(s):  
Yordan Kalmukov ◽  
Milko Marinov ◽  
Tsvetelina Mladenova ◽  
Irena Valova
Keyword(s):  
Experimental Study ◽  
Big Data ◽  
Computer System ◽  
Data Storage ◽  
Fault Tolerant ◽  
Processing System ◽  
Daily Basis ◽  
Distributed File System ◽  
Hadoop Distributed File System ◽  
Big Data Storage

In the age of big data, the amount of data that people generate and use on a daily basis has far exceeded the storage and processing capabilities of a single computer system. That motivates the use of distributed big data storage and processing system such as Hadoop. It provides a reliable, horizontallyscalable, fault-tolerant and efficient service, based on the Hadoop Distributed File System (HDFS) and MapReduce. The purpose of this research is to experimentally determine whether (and to what extent) the network communication speed, the file replication factor, the files’ sizes and their number, and the location of the HDFS client influence the performance of the HDFS read/write operations.

scholarly journals A Real Time Processing system for big data in astronomy: Applications to HERA

2021 ◽  
pp. 100489
Author(s):  
Paul La Plante ◽  
P.K.G. Williams ◽  
M. Kolopanis ◽  
J.S. Dillon ◽  
A.P. Beardsley ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Processing System ◽  
Real Time Processing ◽  
Time Processing

Cloud Query Processing System for Big Data Management

2017 ◽  
pp. 263-288
Author(s):  
Bhavani Thuraisingham ◽  
Mohammad Mehedy Masud ◽  
Pallabi Parveen ◽  
Latifur Khan
Keyword(s):  
Big Data ◽  
Data Management ◽  
Query Processing ◽  
Processing System

Big data application framework and its feasibility analysis in library

2017 ◽  
Vol 45 (4) ◽  
pp. 161-168 ◽  
Author(s):  
Jun Li ◽  
Ming Lu ◽  
Guowei Dou ◽  
Shanyong Wang
Keyword(s):  
Big Data ◽  
Human Resource ◽  
Processing System ◽  
Support Service ◽  
Survey Method ◽  
Application Framework ◽  
Comprehensive Overview ◽  
Content Type ◽  
Data Application ◽  
Big Data Application

Purpose The purpose of this study is to introduce the concept of big data and provide a comprehensive overview to readers to understand big data application framework in libraries. Design/methodology/approach The authors first used the text analysis and inductive analysis method to understand the concept of big data, summarize the challenges and opportunities of applying big data in libraries and further propose the big data application framework in libraries. Then they used questionnaire survey method to collect data from librarians to assess the feasibility of applying big data application framework in libraries. Findings The challenges of applying big data in libraries mainly include data accuracy, data reduction and compression, data confidentiality and security and big data processing system and technology. The opportunities of applying big data in libraries mainly include enrich the library database, enhance the skills of librarians, promote interlibrary loan service and provide personalized knowledge service. Big data application framework in libraries can be considered from five dimensions: human resource, literature resource, technology support, service innovation and infrastructure construction. Most libraries think that the big data application framework is feasible and tend to apply big data application framework. The main obstacles to prevent them from applying big data application framework is the human resource and information technology level. Originality/value This research offers several implications and practical solutions for libraries to apply big data application framework.

scholarly journals Building a fault tolerant framework with deadline guarantee in big data stream computing environments

2017 ◽  
Vol 89 ◽  
pp. 4-23 ◽  
Author(s):  
Dawei Sun ◽  
Guangyan Zhang ◽  
Chengwen Wu ◽  
Keqin Li ◽  
Weimin Zheng
Keyword(s):  
Big Data ◽  
Data Stream ◽  
Fault Tolerant ◽  
Stream Computing ◽  
Computing Environments
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