scholarly journals Power of Big Data System for Storing and Processing Huge Data

2019 ◽  
pp. 138-142
Author(s):  
S. Natarajan ◽  
S. Rajarajesware ◽  
Suresh Ram R
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data System ◽  
Data Systems ◽  
Data Infrastructure ◽  
Work From Home ◽  
Architecture Framework ◽  
Huge Data ◽  
The People

Big data uses storage of huge data with some approaches and techniques to manage and process them. During the past few years the number of persons using internet, email and other internet-based applications has been growing tremendously. Big Data is mainly characterized by 3V’s (Volume, Velocity and, Variety). The Big Data Architecture Framework (BDAF) is proposed to address all aspects of the Big Data Ecosystem. BDAF includes components such as Big Data Infrastructure, Big Data Analytics, Data structures & models, Big Data Lifecycle Management and Big Data Security. Nowadays the volume of data used by the people throughout the world is increasing enormously and exponentially. So, the need for storing, processing and protecting large volume of data has been becoming a great challenge in the modern hyper-connected world. On the basis of work from home concept lot of software professionals are doing their jobs with their internet connected systems for development, implementation, testing and maintenance of various softwares. These professionals and experts are sending and receiving lot of data to various locations to their clients, higher authorities and other officials frequently depending upon their requirements. The traditional data management models are not efficient for today’s exponentially growing data from variety of industries. This challenging task of storing and managing huge volume of data is achieved in Big Data Systems. In this paper we try to give an overview of Big Data Analytics system for storing and processing huge volume of various types of data. Overwhelming the security threats due to various factors like viruses, worms, etc are also great challenges to protect huge volume of data in a big data system.

Big Data Analytics in Supply Chain Management

2021 ◽  
pp. 2443-2457
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

Big Data Analytics

2017 ◽  
Vol 11 (2) ◽  
pp. 41-56 ◽  
Author(s):  
Yingxu Wang ◽  
Jun Peng
Keyword(s):  
Big Data ◽  
Cognitive Processes ◽  
Data Analytics ◽  
Data Science ◽  
Computational Models ◽  
Big Data Analytics ◽  
Data Systems ◽  
Data Engineering ◽  
Big Data Systems ◽  
Cognitive Foundations

Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.

scholarly journals Effect of SBI Bank Merger on Education Loan, a Comparative Study Using Big Data Analytics

2018 ◽  
Vol 7 (2.32) ◽  
pp. 452
Author(s):  
Anjali Mathur ◽  
K Vinitha ◽  
R Shubham ◽  
K Gowtham
Keyword(s):  
Big Data ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Global Level ◽  
Analysis Techniques ◽  
Bank Merger ◽  
Huge Data ◽  
High Level ◽  
The Impact

A bank merger is a situation in which two banks or all branches of a bank join together to become one bank. The bank merger of State Bank of India was implemented on 1stApril 2017 in India. The bank merger is a good idea to centralize the customer’s data from nationwide. However, it is a difficult task for administrators and technologists. Some high level techniques are required to collect the data from the branches, of the bank present at nationwide, and merge them accordingly. For this huge data Big-Data Analysis techniques can be used to manage and access the data. The big data analytics provides algorithms to compare, classify and cluster the data at local and global level. This research paper proposes big data analytics for education loan provided by State Bank of India. The loan granting process becomes centralized after merger. It affects the processing of granting a loan, as earlier it was according to branches only. The proposed work is for comparative study of the impact of bank merger on education loan provided by State Bank of India.  

scholarly journals Analyzation and Comparison of Cloud Computing and Data Mining Techniques: Big Data and Impact of Blockchain

2021 ◽  
Vol 9 (11) ◽  
pp. 712-721
Author(s):  
Adarsh Bhandari
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Scale Up ◽  
Big Data Analytics ◽  
Multiple Sources ◽  
Huge Data ◽  
Amazon Web Services ◽  
Tools And Techniques ◽  
The Impact

Abstract: With the rapid escalation of data driven solutions, companies are integrating huge data from multiple sources in order to gain fruitful results. To handle this tremendous volume of data we need cloud based architecture to store and manage this data. Cloud computing has emerged as a significant infrastructure that promises to reduce the need for maintaining costly computing facilities by organizations and scale up the products. Even today heavy applications are deployed on cloud and managed specially at AWS eliminating the need for error prone manual operations. This paper demonstrates about certain cloud computing tools and techniques present to handle big data and processes involved while extracting this data till model deployment and also distinction among their usage. It will also demonstrate, how big data analytics and cloud computing will change methods that will later drive the industry. Additionally, a study is presented later in the paper about management of blockchain generated big data on cloud and making analytical decision. Furthermore, the impact of blockchain in cloud computing and big data analytics has been employed in this paper. Keywords: Cloud Computing, Big Data, Amazon Web Services (AWS), Google Cloud Platform (GCP), SaaS, PaaS, IaaS.

scholarly journals BIG DATA ANALYTICS IN BANKING AND FINANCIAL SERVICES SECTOR

2020 ◽  
pp. 92-96
Author(s):  
Dr.P.V. Mohini ◽  
Mr. Rohit Kumar Srivastav
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Valuable Data ◽  
Services Sector ◽  
Financial Services Sector ◽  
The People ◽  
Potential Benefits

‘He who holds the wealth of information, holds the solution to the toughest of the situations’ this quote is very much apt for the recent ongoing scenario, where there is an invisible struggle going on among the organizations as well as nations in the search for more and more information. Now, when the Banking and Financial services sectors are put under the scanner, it becomes evident that they are sitting on top of a humungous heap of valuable data. This data can be used for the betterment and advancement of the industry as well as the people. While it is good to have large amount of data available, it will be termed a big pile of trash if it is not analyzed properly and the results obtained from it are not put to use. With the adoption of Big Data analytics into the banking and financial services, many obvious as well as concealed problems can be addressed to and even solved quickly. The main objective of this paper is to highlight the meaning of Big Data analysis, study the types of data analytics with respect to Banking and Financial services sector and to showcase the potential benefits of embracing Big Data analytics into the Banking & Financial services sector. KEYWORDS: Information, Banking and Financial services, Advancement, Big Data, Data Analytics

Integrated Data Architecture for Business

2019 ◽  
pp. 478-490
Author(s):  
Richard Kumaradjaja
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Point Of View ◽  
Future Research ◽  
Integration Framework ◽  
Architecture Framework ◽  
Data Architecture ◽  
Future Research Directions

This chapter describes data integration issues in big data analytics and proposes an integrated data integration framework for big data analytics. The main focus of this chapter is to address the issues of data integration from the architectural point of view. Addressing the issues of data integration from the architectural point of view will lead to a better understanding of the current situation and better construction of proposed solutions to those issues since architectural approach can give us a holistic and comprehensive view of the problems. The chapter also discusses future research directions of the proposed integrated data architecture framework.

Emerging Trends and Techniques in Cloud-Based Data Analytics

2021 ◽  
pp. 37-47
Author(s):  
Shaila S. G. ◽  
Monish L. ◽  
Lavanya S. ◽  
Sowmya H. D. ◽  
Divya K.
Keyword(s):  
Social Media ◽  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Huge Data ◽  
Emerging Trends ◽  
Media Applications

The new trending technologies such as big data and cloud computing are in line with social media applications due to their fast growth and usage. The big data characteristic makes data management challenging. The term big data refers to an immense collection of both organised and unorganised data from various sources, and nowadays, cloud computing supports in storing and processing such a huge data. Analytics are done on huge data that helps decision makers to take decisions. However, merging two conflicting design principles brings a challenge, but it has its own advantage in business and various fields. Big data analytics in the cloud places rigorous demands on networks, storage, and servers. The chapter discusses the importance of cloud platform for big data, importance of analytics in cloud and gives detail insight about the trends and techniques adopted for cloud analytics.

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