Data Storage Adapter in Big Data Platform

Author(s):  
Minh Chau Nguyen ◽  
Hee Sun Won
Keyword(s):  
Big Data ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 36
Author(s):  
Sajeewan Pratsri ◽  
Prachyanun Nilsook

According to a continuously increasing amount of information in all aspects whether the sources are retrieved from an internal or external organization, a platform should be provided for the automation of whole processes in the collection, storage, and processing of Big Data. The tool for creating Big Data is a Big Data challenge. Furthermore, the security and privacy of Big Data and Big Data analysis in organizations, government agencies, and educational institutions also have an impact on the aspect of designing a Big Data platform for higher education institute (HEi). It is a digital learning platform that is an online instruction and the use of digital media for educational reform including a module provides information on functions of various modules between computers and humans. 1) Big Data architecture is a framework for an architecture of numerous data which consisting of Big Data Infrastructure (BDI), Data Storage (Cloud-based), processing of a computer system that uses all parts of computer resources for optimal efficiency (High-Performance Computing: HPC), a network system to detect the target device network. Thereafter, according to Hadoop’s tools and techniques, when Big Data was introduced with Hadoop's tools and techniques, the benefits of the Big Data platform would provide desired data analysis by retrieving existing information, to illustrate, student information and teaching information that is large amounts of information to adopt for accurate forecasting.


2019 ◽  
Author(s):  
Cesar Navarro-Paredes ◽  
Min Jing ◽  
Dewar Finlay ◽  
James McLaughlin

2018 ◽  
Vol 2 (3) ◽  
pp. 169
Author(s):  
Manishankar S ◽  
S. Sathayanarayana

In this Digital world storage area capacity required for an Enterprise is quite huge, and processing that Big Data is one of the major challenging areas in today’s information technology. As the heterogeneous data from the various sources grow rapidly, there should be some proficient way for data storage for each enterprise. Most of the Enterprises have a tendency to migrate their data in to servers with high processing capability to handle variety and voluminous data. Major problem that arises in such big data servers of an Enterprise is the process involved in segregating data according to their types. In this research, an efficient methodology is proposed which handles the segregation of data inside a server with multi valued distribution-based clustering. These clustering-based solutions provide an efficient visualization of varying data in the server and also a separate visualization of employee data too. The paper discusses about the simulation of the clustering technique with respect to an Enterprise data and visualization of file storage structure and categorization of data, also it gives a picture of performance of the Big data server. 


Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.


Author(s):  
Karima Aslaoui Mokhtari ◽  
Salima Benbernou ◽  
Mourad Ouziri ◽  
Hakim Lahmar ◽  
Muhammad Younas

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