scholarly journals Big Data Challenges and Learning Paradigms: A Review

2021 ◽  
Vol 23 (12) ◽  
pp. 36-45
Author(s):  
S. L SWAPNA ◽  
◽  
V. SARAVANAN ◽  
Keyword(s):  
Big Data ◽  
Scientific Method ◽  
Data Analytics ◽  
Review Paper ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Information Revolution ◽  
Business Organizations ◽  
Using Data

Big data is one of the impacts of information revolution due to technological advancements such as communication, mobile and cloud services. The uncontrolled accumulation of structured and unstructured enormous volumes of data creates challenges in storing and manipulating data and obtaining valuable insights from these data. Big Data Analytics is progressively becoming popular and the organizations are in forefront to devise and adopt diversified approaches including machine learning for Big Data Analytics. Business organizations are using data learning as a scientific method for dealing with big data. The use of appropriate data analytics tools is crucial for the organizations to withstand in their business, to face the challenges in the market and gain out of competitive advantage. By considering the overwhelming demand on the data analytics tools, this review paper presents the comprehensive view on various Big Data Analytics methods in place and the state-of-the-art approaches towards Big Data Analytics. This paper also presents upcoming challenges towards big data and suggests certain mechanisms to thwart those challenges.

IoT Based Agriculture as a Cloud and Big Data Service

2020 ◽  
pp. 1499-1521
Author(s):  
Sukhpal Singh Gill ◽  
Inderveer Chana ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Cloud Environment ◽  
Big Data Technologies ◽  
Sensor Networking ◽  
New Applications

Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.

scholarly journals Novel holistic architecture for analytical operation on sensory data relayed as cloud services

2020 ◽  
Vol 10 (4) ◽  
pp. 4322
Author(s):  
Manujakshi B. C ◽  
K. B. Ramesh
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Cloud Services ◽  
Sensor Nodes ◽  
Sensory Data ◽  
Energy Constrained ◽  
Analytical Operation

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

Privacy-Preserving in Big Data Analytics: State of the Art

2020 ◽  
Author(s):  
Hidayath Ali Baig ◽  
Dr. Yogesh Kumar Sharma ◽  
Syed Zakir Ali
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Privacy Preserving

Big Data and Its Role in Facilitating the Visualization of Financial Analytics

2018 ◽  
pp. 704-722
Author(s):  
Iman Raeesi Vanani ◽  
Maziar Shiraj Kheiri
Keyword(s):  
Big Data ◽  
Global Economy ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Accounting Measurement ◽  
Use Of Data ◽  
Accounting Environment ◽  
Using Data ◽  
And Function ◽  
Support Decision Making

The business use of data analytics is growing rapidly in the accounting environment. Similar to many new systems that involve accounting information, data analytics has fundamentally changed task based processes particularly those tasks that provide inference, prediction and assurance to decision makers. Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making. Big Data now pervades every sector and function of the global economy. These essays focus on the uses and challenges of Big Data in accounting (measurement) and auditing (assurance). The objective of this chapter is to examine how Big Data analytics will impact the accounting and auditing environment. This is important to practitioners as well as academics because they will be using data analytics in accounting and auditing tasks and will need to have an in-depth familiarity with financial analytics to effectively accomplish these tasks and make effective and efficient decisions.

scholarly journals Economic Supply and Demand with Data Analytics Functionalities

2020 ◽  
pp. 13-18
Author(s):  
Richard C. Gambo
Keyword(s):  
Big Data ◽  
System Dynamics ◽  
Data Analytics ◽  
State Of The Art ◽  
Supply And Demand ◽  
Current Paper ◽  
Knowledge Generation ◽  
Dynamics Modeling ◽  
Using Data ◽  
Analytical Approaches

The primary goal of this article is to start a discussion about the possibility to connect supply and demand with data analytics functionalities in the frame of a dynamic system environment. So, a number of classical supply and demand topics, concepts, and definitions, as well as state-of-the-art data analytics concepts are reviewed firstly. Then, the critical modeling problem of both concepts “supply” and “demand” using system dynamics is introduced, analyzed, and examined. Finally, supply, demand, big data and data analytics are considered in a system dynamics modeling environment. Actually, the proposed paper provides an initial approach (introduction) to the main (basic) procedures, analytical approaches and methods of data and big data analysis. In particular, a framework to help program staff in their job and approaches on supply and demand issues using big data procedures and methods is presented. Accordingly, this article aims to support the work of data analytics and statistics staff across various content areas with big data functionalities. This article was created because the state-of-the-art concept “using data and information in meaningful and smart ways” includes many opportunities and possibilities and obviously a great deal of information is involved. Doubtless, some of this information has a great complexity and it is highly dependent upon specialized data, information and knowledge like the “data analytics” concept. However, there are many ways of “using data in smart ways” that are more primitive and that involve relatively simple enough procedures. Hence, the purpose of the current paper is to provide data analytics functionalities in supply and demand applications with a contemporary framework for thinking about, working with, and benefiting from an increased ability to use big data smartly and efficiently. Finally, the current paper should be characterized as a knowledge generation opinion article which recommends the inclusion of data analytics and distributed technology in supply and demand industry in order to enhance functionalities and compatibility to state-of-the-art ICT.

Big Data Management

2017 ◽  
Vol 7 (3) ◽  
pp. 44-55 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Business Leaders ◽  
Privacy And Security ◽  
Review Analysis ◽  
Using Data

The objective of this article is to provide the advanced issues and approaches of big data management. The literature review indicates the overview of big data management; the aspects of Big Data Analytics (BDA); the importance of big data management; the methods for big data management; the privacy and security concerns of big data management; and the big data management in the health care industry. Organizations that have been successful in working with effective big data management have accomplished this issue using data to help make sense of the information. The volume of data that companies are able to gather about customers and market conditions can provide business leaders with insights into new revenue and business opportunities, presuming they can spot the opportunities in vast amounts of data. The literature review analysis provides both practitioners and researchers an important understanding about big data management in modern organizations.

Critical Comprehensive Study of Big Data Analytics Management

2021 ◽  
pp. 235-247
Author(s):  
Mahfuzul Huda ◽  
Mohamed I Habib ◽  
Mohammad Zubair Khan ◽  
Abdullah Abdul Aziz
Keyword(s):  
Big Data ◽  
Regression Analysis ◽  
Associate Degree ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Organizations ◽  
Specific Data ◽  
Actionable Knowledge ◽  
Analytical Strategies

Big data helps drive quality, customize many services, satisfy clients, and increase profit in business organizations. This chapter discusses advanced analytical strategies, together with the data classification, multivariate and regression analysis on interpreting the text analysis and specific data management technologies and many tools that scientifically support advanced data analytics management with big data knowledge. This work also highlighted the deliverables, converting degree associate analytics project to associate degree in progress quality of a data organization's management operation, and making helpful, clear, and visual analytical outputs supported on the basis of actionable knowledge.

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