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.

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.

Insight Into Big Data Analytics

2018 ◽  
pp. 67-79
Author(s):  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Imran
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Huge Amount ◽  
Business Decision ◽  
Digital Era ◽  
Data Formats ◽  
Recent Trends ◽  
Analytical Approaches

In the present digital era, more data are generated and collected than ever before. But, this huge amount of data is of no use until it is converted into some useful information. This huge amount of data, coming from a number of sources in various data formats and having more complexity, is called big data. To convert the big data into meaningful information, the authors use different analytical approaches. Information extracted, after applying big data analytics methods over big data, can be used in business decision making, fraud detection, healthcare services, education sector, machine learning, extreme personalization, etc. This chapter presents the basics of big data and big data analytics. Big data analysts face many challenges in storing, managing, and analyzing big data. This chapter provides details of challenges in all mentioned dimensions. Furthermore, recent trends of big data analytics and future directions for big data researchers are also described.

Improving the use of analytics and big data by changing the decision-making culture

2017 ◽  
Vol 55 (10) ◽  
pp. 2074-2088 ◽  
Author(s):  
Jane Elisabeth Frisk ◽  
Frank Bannister
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Approach ◽  
Action Research Project ◽  
Content Type ◽  
Use Of Data ◽  
Support Organizations ◽  
Novel Approach ◽  
Using Data

Purpose Evolving digital technologies continue to enable new ways to collect and analyze data and this has led some researchers to claim that skillful use of data analytics and big data can radically improve a company’s performance, but that in order to achieve such improvements managers need to change their decision-making culture and to increase the degree of collaboration in the decision-making process. The purpose of this paper is to create an increased understanding of how a decision-making culture can be changed by using a design approach. Design/methodology/approach The paper presents an action research project in which the authors use a design approach. Findings By adopting a design approach organizations can change their decision-making culture, increase the degree of collaboration and also reduce the influence of power and politics on their decision-making. Research limitations/implications This paper proposes a new approach to changing a decision-making culture. Practical implications Using data analytics and big data, a design approach can support organizations change their decision-making culture resulting in better and more effective decisions. Originality/value This paper bridges design and decision-making theory in a novel approach to an old problem.

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 SYSTEM DYNAMICS MODELING FOR CONSTRUCTION MANAGEMENT RESEARCH: CRITICAL REVIEW AND FUTURE TRENDS

2019 ◽  
Vol 25 (8) ◽  
pp. 730-741 ◽  
Author(s):  
Mingqiang Liu ◽  
Yun Le ◽  
Yi Hu ◽  
Bo Xia ◽  
Martin Skitmore ◽  
...  
Keyword(s):  
System Dynamics ◽  
Strategic Management ◽  
Construction Management ◽  
State Of The Art ◽  
Project Planning ◽  
System Dynamics Modeling ◽  
Dynamics Modeling ◽  
Planning And Control ◽  
And Control ◽  
The Impact

As a result of growing complexities in the construction industry, system dynamics modeling (SDM) has been increasingly used in construction management (CM) research to explore complicated causal relationships at the various levels of construction and management processes. Given the rapid growth of SDM applications over the past two decades, a systematic review is needed to ascertain the state of the art and further trends in the area. This paper provides the results of a systematic analysis of 103 papers from 41 selected peer-reviewed journals from 1997 to 2016. The contributions of the papers are first analyzed, structured and formulated in terms of the year of publication, software involved, the combined use with other methods, and research design. With the assistance of the a keyword co-occurrence network analysis, eight research topics involving different internal and external complexities are identified, including: (1) sustainability, (2) project planning and control, (3) performance and effectiveness, (4) strategic management, (5) site and resource management, (6) risk analysis and management, (7) knowledge management, and (8) organization and stakeholder management. The analysis results reveal the pivotal role of SDM in streamlining different complicated casual relationships at the activity, project, and industry levels across the eight topics and its significant potential in uncovering the impact of complicated contextual conditions on project planning and control, effectiveness and performance, strategic management, and sustainability at the project and industry levels. Lastly, trends and recommendations for SDM applications are provided for future CM research. This paper provides a state of the art of SDM in CM applications and insights into opportunities and useful references for the future.

scholarly journals Data Analytics: COVID-19 Prediction Using Multimodal Data

2020 ◽  
Author(s):  
Parikshit N. Mahalle ◽  
Nilesh P. Sable ◽  
Namita P. Mahalle ◽  
Gitanjali R. Shinde
Keyword(s):  
Data Analytics ◽  
Predictive Analytics ◽  
State Of The Art ◽  
Time Span ◽  
Cumulative Number ◽  
Short Term ◽  
Multimodal Data ◽  
Short Time Span ◽  
Using Data ◽  
Short Time

Globally, there is massive uptake and explosion of data and challenge is to address issues like scale, pace, velocity, variety, volume and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 towards epidemiological triad and the study of state-of-the-art. The main aim this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time.

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.

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