scholarly journals Financial Big Data Analysis and Early Warning Platform: A Case Study

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 36515-36526 ◽  
Author(s):  
Yi Liang ◽  
Daiyong Quan ◽  
Fang Wang ◽  
Xiaojun Jia ◽  
Menggang Li ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Early Warning ◽  
Big Data Analysis

Data Science and Big Data Analytics in Financial Services

Web Services ◽  
2019 ◽  
pp. 1301-1329
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Financial Services ◽  
Data Science ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Big Data Analysis ◽  
Future Trends ◽  
The Right

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

scholarly journals Enhancing Big Data Analysis by using Map-reduce Technique

2018 ◽  
Vol 7 (1) ◽  
pp. 113-116
Author(s):  
Alaa Hussein Al-Hamami ◽  
Ali Adel Flayyih
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Response Time ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Map Reduce ◽  
Fast Method ◽  
Traditional Methods ◽  
Electroencephalogram Eeg

Database is defined as a set of data that is organized and distributed in a manner that permits the user to access the data being stored in an easy and more convenient manner. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work enhances the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on Electroencephalogram (EEG) Big-data as a case study. The proposed approach showed clear enhancement on managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG researchers and specialist with an easy and fast method of handling the EEG big data.

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