Securing Online Bank's Big Data Through Block Chain Technology

2020 ◽  
pp. 247-263 ◽  
Author(s):  
Kommu Narendra ◽  
G. Aghila
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Financial Services ◽  
Data Analytics ◽  
Big Data Processing ◽  
Cross Border ◽  
Processing Data ◽  
The World ◽  
Block Chain ◽  
New Challenges

Many sectors and fields are being computerized to make the work paperless, more transparent, and efficient. Banking is one such sector that has undergone enormous changes. Any amount from any part to any corner of the world is now possible around the clock. The dependency on technology for providing the services necessitates security, and the additional risks involved in cross-border nature of transactions of banks poses new challenges for banking regulators and supervisors. Many types of research are going in this area of banks big data processing, data analytics, and providing security for cross-border payments to mitigate the risks. Block chain is one such advancement for addressing the challenges in financial services. This chapter provides a brief overview of block chain usage, addressing the traditional issues and challenges for cross-border transactions.

scholarly journals Agile Elastic Desktop Corporate Architecture for Big Data

2020 ◽  
Vol 20 (3) ◽  
pp. 15-31
Author(s):  
Valentin Kisimov ◽  
Dorina Kabakchieva ◽  
Aleksandar Naydenov ◽  
Kamelia Stefanova
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Business Environment ◽  
Digital Transformation ◽  
Big Data Processing ◽  
Online Business ◽  
New Challenges ◽  
Corporate Architecture ◽  
Effective Use ◽  
Corporate Business

AbstractNew challenges in the dynamically changing business environment require companies to experience digital transformation and more effective use of Big Data generated in their expanding online business activities. A possible solution for solving real business problems concerning Big Data resources is proposed in this paper. The defined Agile Elastic Desktop Corporate Architecture for Big Data is based on virtualizing the unused desktop resources and organizing them in order to serve the needs of Big Data processing, thus saving resources needed for additional infrastructure in an organization. The specific corporate business needs are analyzed within the developed R&D environment and, based on that, the unused desktop resources are customized and configured into required Big Data tools. The R&D environment of the proposed Agile Elastic Desktop Corporate Architecture for Big Data could be implemented on the available unused resources of hundreds desktops.

Modeling Big Data Analytics with a Real-Time Executable Specification Language

2015 ◽  
pp. 289-312
Author(s):  
Amir A. Khwaja
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Exception Handling ◽  
Specification Language ◽  
Big Data Processing ◽  
Business Decisions ◽  
Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

Big Data Analytics in Cloud Computing

2019 ◽  
pp. 325-341
Author(s):  
Rajganesh Nagarajan ◽  
Ramkumar Thirunavukarasu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Big Data Processing ◽  
Investment Cost

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.

Modeling Big Data Analytics with a Real-Time Executable Specification Language

Big Data ◽  
2016 ◽  
pp. 418-440
Author(s):  
Amir A. Khwaja
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Exception Handling ◽  
Specification Language ◽  
Big Data Processing ◽  
Business Decisions ◽  
Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

A Study of Big Data Processing for Sentiments Analysis

2022 ◽  
pp. 1162-1191
Author(s):  
Dinesh Chander ◽  
Hari Singh ◽  
Abhinav Kirti Gupta
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Analytics ◽  
Important Application ◽  
Streaming Data ◽  
Test Case ◽  
Big Data Processing ◽  
Important Field ◽  
Batch Data ◽  
Different Sources

Data processing has become an important field in today's big data-dominated world. The data has been generating at a tremendous pace from different sources. There has been a change in the nature of data from batch-data to streaming-data, and consequently, data processing methodologies have also changed. Traditional SQL is no longer capable of dealing with this big data. This chapter describes the nature of data and various tools, techniques, and technologies to handle this big data. The chapter also describes the need of shifting big data on to cloud and the challenges in big data processing in the cloud, the migration from data processing to data analytics, tools used in data analytics, and the issues and challenges in data processing and analytics. Then the chapter touches an important application area of streaming data, sentiment analysis, and tries to explore it through some test case demonstrations and results.

scholarly journals Attributes Reduction in Big Data

2020 ◽  
Vol 10 (14) ◽  
pp. 4901
Author(s):  
Waleed Albattah ◽  
Rehan Ullah Khan ◽  
Khalil Khan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Data Analytics ◽  
Detection Efficiency ◽  
Large Data ◽  
Model Learning ◽  
Big Data Processing ◽  
Performance Loss ◽  
Points Of View

Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics.

Big Data Overview

Big Data ◽  
2016 ◽  
pp. 1-29 ◽  
Author(s):  
Yushi Shen ◽  
Yale Li ◽  
Ling Wu ◽  
Shaofeng Liu ◽  
Qian Wen
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Processing ◽  
Data Scientist ◽  
Definition Of

This chapter provides an overview of big data and its environment and opportunities. It starts with a definition of big data and describes the unique characteristics, structure, and value of big data, and the business drivers for big data analytics. It defines the role of the data scientist and describes the new ecosystem for big data processing and analysis.

A Survey on Big Data Processing Frameworks for Mobility Analytics

2021 ◽  
Vol 50 (2) ◽  
pp. 18-29
Author(s):  
Christos Doulkeridis ◽  
Akrivi Vlachou ◽  
Nikos Pelekis ◽  
Yannis Theodoridis
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Management ◽  
Spatial Data ◽  
State Of The Art ◽  
Temporal Data ◽  
Big Data Processing ◽  
Mobility Data ◽  
New Challenges ◽  
Spatio Temporal

In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.

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