scholarly journals Big Data as a Creeping Crisis

2021 ◽  
pp. 69-86
Author(s):  
Swapnil Vashishtha ◽  
Mark Rhinard
Keyword(s):  
Big Data ◽  
Driving Forces ◽  
Government Intervention ◽  
Personal Integrity ◽  
Public Attention ◽  
Horizon Scanning ◽  
Main Challenge ◽  
Private Data ◽  
Mass Accumulation

AbstractThis chapter examines the mass accumulation of private data in terms of a creeping crisis. The threat at hand—commonly referred to as “Big Data”—pertains to the direct compromising of personal integrity and safety. The chapter explores the driving forces behind this threat, identifies the precursor events or “flare-ups” of the deeper problem, and documents the varying levels of scientific, political, and public attention given to the problem. Our analysis reveals the breadth of the problem and the main challenge to managing it: societies’ deep dependence on the underlying technologies and systems. Addressing this creeping crisis will require substantial government intervention to regulate privacy and effective horizon scanning to track its many possible costs.

scholarly journals A News Big Data Analysis of Issues in Higher Education in Korea amid the COVID-19 Pandemic

2021 ◽  
Vol 13 (13) ◽  
pp. 7347
Author(s):  
Jangwan Ko ◽  
Seungsu Paek ◽  
Seoyoon Park ◽  
Jiwoo Park
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analysis ◽  
College Education ◽  
Big Data Analysis ◽  
Second Phase ◽  
Public Attention ◽  
Job Market ◽  
Media Reports ◽  
The Government

This paper examines the main issues regarding higher education in Korea—where college education experienced minimal interruptions—during the COVID-19 pandemic through a big data analysis of news articles. By analyzing policy responses from the government and colleges and examining prominent discourses on higher education, it provides a context for discussing the implications of COVID-19 on education policy and what the post-pandemic era would bring. To this end, we utilized BIgKinds, a big data research solution for news articles offered by the Korea Press Foundation, to select a total of 2636 media reports and conducted Topic Modelling based on LDA algorithms using NetMiner. The analyses are split into three distinct periods of COVID-19 spread in the country. Some notable topics from the first phase are remote class, tuition refund, returning Chinese international students, and normalization of college education. Preparations for the College Scholastic Ability Test (CSAT), contact and contactless classes, preparations for early admissions, and supporting job market candidates are extracted for the second phase. For the third phase, the extracted topics include CSAT and college-specific exams, quarantine on campus, social relations on campus, and support for job market candidates. The results confirmed widespread public attention to the relevant issues but also showed empirically that the measures taken by the government and college administrations to combat COVID-19 had limited visibility among media reports. It is important to note that timely and appropriate responses from the government and colleges have enabled continuation of higher education in some capacity during the pandemic. In addition to the media’s role in reporting issues of public interest, there is also a need for continued research and discussion on higher education amid COVID-19 to help effect actual results from various policy efforts.

Good games, bad host? Using big data to measure public attention and imagery of the Olympic Games

Cities ◽  
2019 ◽  
Vol 90 ◽  
pp. 229-236 ◽  
Author(s):  
Eva Kassens-Noor ◽  
Joshua Vertalka ◽  
Mark Wilson
Keyword(s):  
Big Data ◽  
Olympic Games ◽  
Public Attention ◽  
The Olympic Games

scholarly journals Use cases and challenges in telecom big data analytics

2016 ◽  
Vol 5 ◽  
Author(s):  
Chung-Min Chen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Driving Forces ◽  
Big Data Analytics ◽  
Use Cases ◽  
Telecommunication Industry ◽  
New Research

This paper examines the driving forces of big data analytics in the telecom domain and the benefits it offers. We provide example use cases of big data analytics and the associated challenges, with the hope to inspire new research ideas that can eventually benefit the practice of the telecommunication industry.

scholarly journals Big data and privacy: The study of privacy invasion acceptance in the world of big data

2017 ◽  
Vol 5 (1) ◽  
pp. 57-71
Author(s):  
Jędrzej Wieczorkowski ◽  
Przemysław Polak
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Personal Data ◽  
Social Consequences ◽  
Social Aspect ◽  
Commercial Use ◽  
Private Data ◽  
The Social ◽  
Business Application ◽  
Privacy Violation

The phenomenon of big data includes technological (new opportunities), business (application), and social aspect. The social aspect applies to the social consequences of the use of big data methods, in particular, those related to the processing of personal and other private data , as well as the danger of privacy violation. In the context of the big data phenomenon, this study presents the results of a survey on the level of acceptance of privacy violation resulting from mass data processing. The different objectives of processing were taken into account, including general, social and commercial. This study helps to draw conclusions concerning commercial and non-commercial use of private data, as well as the legal regulations on personal data processing.

scholarly journals Agriculture Data Analytics in Crop Yield Estimation: A Critical Review

2018 ◽  
Vol 12 (3) ◽  
pp. 1087 ◽  
Author(s):  
B.M. Sagar ◽  
Cauvery N K
Keyword(s):  
Big Data ◽  
Crop Yield ◽  
Data Analytics ◽  
Crop Production ◽  
Big Data Analytics ◽  
Agricultural Practices ◽  
Climatic Conditions ◽  
Yield Prediction ◽  
Main Challenge ◽  
Crop Health

<p>Agriculture is important for human survival because it serves the basic need. A well-known fact that the majority of population (≥55%) in India is into agriculture. Due to variations in climatic conditions, there exist bottlenecks for increasing the crop production in India. It has become challenging task to achieve desired targets in Agri based crop yield. Factors like climate, geographical conditions, economic and political conditions are to be considered which have direct impact on the production, productivity of the crops. Crop yield prediction is one of the important factors in agriculture practices. Farmers need information regarding crop yield before sowing seeds in their fields to achieve enhanced crop yield. The use of technology in agriculture has increased in recent year and data analytics is one such trend that has penetrated into the agriculture field being used for management of crop yield and monitoring crop health. The recent trends in the domain of agriculture have made the people to understand the significance of          Big data. The main challenge using big data in agriculture is identification of impact and effectiveness of big data analytics.  Efforts are going on to understand how big data analytics can be used to improve the productivity in agricultural practices. The analysis of data related to agriculture helps in crop yield prediction, crop health monitoring and other such related activities. In literature, there exist several studies related to the use of data analytics in the agriculture domain. The present study gives insights on various data analytics methods applied to crop yield prediction. The work also signifies the important lacunae points’ in the proposed area of research.</p>

scholarly journals Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

2019 ◽  
Vol 8 (7) ◽  
pp. 314 ◽  
Author(s):  
Qiushi Gu ◽  
Haiping Zhang ◽  
Min Chen ◽  
Chongcheng Chen
Keyword(s):  
Big Data ◽  
Concentration Index ◽  
Driving Forces ◽  
Regional Differentiation ◽  
Geographical Concentration ◽  
Source Regions ◽  
Short Period ◽  
Tourist Flows ◽  
Travel Rate ◽  
Source And Sink

At present, population mobility for the purpose of tourism has become a popular phenomenon. As it becomes easier to capture big data on the tourist digital footprint, it is possible to analyze the respective regional features and driving forces for both tourism sources and destination regions at a macro level. Based on the data of tourist flows to Nanjing on five short-period national holidays in China, this study first calculated the travel rate of tourist source regions (315 cities) and the geographical concentration index of the visited attractions (51 scenic spots). Then, the spatial autocorrelation metrics index was used to analyze the global autocorrelation of the travel rates of tourist source regions and the geographical concentration index of the tourist destinations on five short-term national holidays. Finally, a heuristic unsupervised machine-learning method was used to analyze and map tourist sources and visited attractions by adopting the travel rate and the geographical concentration index accordingly as regionalized variables. The results indicate that both source and sink regions expressed distinctive regional differentiation patterns in the corresponding regional variables. This study method provides a practical tool for analyzing regionalization of big data in tourist flows, and it can also be applied to other origin-destination (OD) studies.

Organizational Design of Big Data and Analytics Teams

2018 ◽  
Vol 5 (3) ◽  
pp. 132-149 ◽  
Author(s):  
Lennart Hammerström
Keyword(s):  
Big Data ◽  
High Performance ◽  
Organizational Design ◽  
Data Set ◽  
Main Challenge ◽  
Organizational Integration ◽  
Effective Use ◽  
Flow Of Information ◽  
Self Learning ◽  
Specific Education

Abstract Although many would argue that the most important factor for the success of a big data project is the process of analyzing the data, it is more important to staff, structure and organize the participants involved to ensure an efficient collaboration within the team and an effective use of the tool sets, the relevant applications and a customized flow of information. A main challenge of big data projects originates from the amount of people involved and that need to collaborate, the need for a higher and specific education, the defined approach to solve the analytical problem that is undefined in many cases, the data-set itself (structured or unstructured) and the required hard- and software (such as analysis-software or self-learning algorithms). Today there is neither an organizational framework nor overarching guidelines for the creation of a high-performance analytics team and its organizational integration available. This paper builds upon (a) the organizational design of a team for a big data project, (b) the relevant roles and competencies (such as programming or communication skills) of the members of the team and (c) the form in which they are connected and managed.

Differential Privacy Approach for Big Data Privacy in Healthcare

2017 ◽  
pp. 191-213 ◽  
Author(s):  
Marmar Moussa ◽  
Steven A. Demurjian
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Data Privacy ◽  
Large Scale ◽  
Differential Privacy ◽  
Security And Privacy ◽  
Instrumental Case Study ◽  
Large Scale Data ◽  
Private Data

This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases often used in healthcare. A case study is presented utilizing differential privacy in healthcare domain, the chapter analyzes and compares the major differentially private data release strategies and noise mechanisms such as the Laplace and the exponential mechanisms. The background section discusses several security and privacy approaches in big data including authentication and encryption protocols, and privacy preserving techniques such as k-anonymity. Next, the chapter introduces the differential privacy concepts used in the interactive and non-interactive data sharing models and the various noise mechanisms used. An instrumental case study is then presented to examine the effect of applying differential privacy in analytics. The chapter then explores the future trends and finally, provides a conclusion.

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