A New Web-Based Big Data Analytics for Dynamic Public Opinion Mapping in Digital Networks on Contested Biotechnology Fields

2020 ◽  
Vol 24 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Virginie Tournay ◽  
Mathieu Jacomy ◽  
Andra Necula ◽  
Annette Leibing ◽  
Alessandro Blasimme
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Networks ◽  
Web Based

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks

Big Data ◽  
2016 ◽  
pp. 1403-1420 ◽  
Author(s):  
Yingxu Wang ◽  
Victor J. Wiebe
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Computational Intelligence ◽  
Data Analytics ◽  
Collective Intelligence ◽  
Big Data Analytics ◽  
Fuzzy Data ◽  
Web Based ◽  
Data Engineering ◽  
Computational Sociology

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

An Analysis of Big Data Analytics

2021 ◽  
pp. 203-230
Author(s):  
Vijander Singh ◽  
Amit Kumar Bairwa ◽  
Deepak Sinwar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Past Century ◽  
Data Generation ◽  
Microsoft Excel ◽  
Web Based ◽  
The Past ◽  
Digital World ◽  
World Information

In the development of the advanced world, information has been created each second in numerous regions like astronomy, social locales, medical fields, transportation, web-based business, logical research, horticulture, video, and sound download. As per an overview, in 60 seconds, 600+ new clients on YouTube and 7 billion queries are executed on Google. In this way, we can say that the immense measure of organized, unstructured, and semi-organized information are produced each second around the cyber world, which should be managed efficiently. Big data conveys properties such as unpredictability, 'V' factor, multivariable information, and it must be put away, recovered, and dispersed. Logical arranged data may work as information in the field of digital world. In the past century, the sources of data as to size were very limited and could be managed using pen and paper. The next generation of data generation tools include Microsoft Excel, Access, and database tools like SQL, MySQL, and DB2.

Agile Big Data Analytics for Web-Based Systems: An Architecture-Centric Approach

2016 ◽  
Vol 2 (3) ◽  
pp. 234-248 ◽  
Author(s):  
Hong-Mei Chen ◽  
Rick Kazman ◽  
Serge Haziyev
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Web Based

Big Data Analytics: A Case Study of Public Opinion Towards the Adoption of Driverless Cars

2018 ◽  
pp. 347-351
Author(s):  
Rasmus Buch ◽  
Samaneh Beheshti-Kashi ◽  
Thomas Alexander Sick Nielsen ◽  
Aseem Kinra
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Driverless Cars

Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks

2014 ◽  
Vol 8 (3) ◽  
pp. 29-44 ◽  
Author(s):  
Yingxu Wang ◽  
Victor J. Wiebe
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Data Analytics ◽  
Collective Intelligence ◽  
Big Data Analytics ◽  
Fuzzy Data ◽  
Web Based ◽  
Fuzzy Models ◽  
Data Engineering ◽  
Computational Sociology

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

Application of Big Data Techniques for Efficient Web-Based Library Services Using Big Data

2021 ◽  
pp. 58-77
Author(s):  
Amita S. Pradhan ◽  
Swapnaja Rajesh Hiray
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Information Science ◽  
Big Data Analytics ◽  
Digital Data ◽  
Library And Information Science ◽  
Data Sets ◽  
Web Based ◽  
User Query ◽  
On Line

Information communication technology is growing at a faster speed and diversified way. Cloud computing, internet of things, 5G, and such technologies are gearing-up and resulting in proliferation of data. Data is a raw information and it has created a big data. To handle such data, big data techniques are emerging. Library and information science is a big-way service profession that mediates between the data/information and the users, letting them be students, researchers, technocrats. Big data, mostly digital data, is being generated through multiple on-line surveys and repositories. Digital and social media is the main source of generating such data. Analyzing such data according to user needs is a huge task. This is the challenge now a days to organize the data explosion, specially the volume and variety of data. Big data analytics proves to be a major help in organizing and fetching data sets pertaining to user query. The authors, in this chapter, deal with four major services of libraries, wherein time efficiency can be achieved through big data analytics. Authors have focused on thrust areas of library and information science and indicate the benefits of big data analytics for service efficiency.

An Analysis of Big Data Analytics

2022 ◽  
pp. 1126-1148
Author(s):  
Vijander Singh ◽  
Amit Kumar Bairwa ◽  
Deepak Sinwar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Past Century ◽  
Data Generation ◽  
Microsoft Excel ◽  
Web Based ◽  
The Past ◽  
Digital World ◽  
World Information

In the development of the advanced world, information has been created each second in numerous regions like astronomy, social locales, medical fields, transportation, web-based business, logical research, horticulture, video, and sound download. As per an overview, in 60 seconds, 600+ new clients on YouTube and 7 billion queries are executed on Google. In this way, we can say that the immense measure of organized, unstructured, and semi-organized information are produced each second around the cyber world, which should be managed efficiently. Big data conveys properties such as unpredictability, 'V' factor, multivariable information, and it must be put away, recovered, and dispersed. Logical arranged data may work as information in the field of digital world. In the past century, the sources of data as to size were very limited and could be managed using pen and paper. The next generation of data generation tools include Microsoft Excel, Access, and database tools like SQL, MySQL, and DB2.

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