scholarly journals Big Data Technologies in Political Processes: Risks and Opportunities

2020 ◽  
Vol 9 (6) ◽  
pp. 143-149 ◽  
Author(s):  
D. R. Mukhametov
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Political Institutions ◽  
Personal Data ◽  
Data Driven ◽  
Grey Zone ◽  
Political Processes ◽  
Personal Data Protection ◽  
Big Data Technologies ◽  
Political Agents

The article deals with various aspects related to the use of Big Data technologies in political processes. Digital technologies have an ambivalent impact on the social and political processes, creating the “grey zone” of opportunities and resources that are the subject of conflicts and competition among various political agents. This statement is equally true concerning election campaigns. Firstly, the author describes the concept of data-driven campaign, which is rapidly spreading due to the demand for flexible management mechanisms and the formation of the “attention economy”. The implementation of the concept includes processes of data mining and analysis, microtargeting — the article reveals the content of each stage on the example of recent cases. The essential advantage of using big data analysis in political processes is concluded not only in the scale of the data mining but also in the possibility to examine deep causal relationships and dependencies, which extends the range of opportunities to influence political agents behaviour. Secondly, it is possible to extrapolate mechanisms of data-driven campaign to the level of data-driven politics. The author formulates the major risks and threats associated with the use of Big Data in political processes: funnel of mistrust in political institutions and technologies, blurring political institutions and plebiscite democracy, the preservation and confidentiality of personal data, the consequences of algorithms cognitive restrictions. As a result, in the short term it will be relevant to provide institutional regulation of data using, as well as to support the development of human capital as the basic skills of personal data protection and the use of modern technologies.

Big Data, Anonymisation and Governance to Personal Data Protection

2020 ◽  
Author(s):  
Artur Potiguara Carvalho ◽  
Fernanda Potiguara Carvalho ◽  
Edna Dias Canedo ◽  
Pedro Henrique Potiguara Carvalho
Keyword(s):  
Big Data ◽  
Data Protection ◽  
Personal Data ◽  
Personal Data Protection

scholarly journals Digital Economy, Big Data and Competition Law

2019 ◽  
Vol 3 (1) ◽  
pp. 53-89
Author(s):  
Roberto Augusto Castellanos Pfeiffer
Keyword(s):  
Big Data ◽  
Data Protection ◽  
Personal Data ◽  
Competition Law ◽  
Digital Economy ◽  
Data Driven ◽  
Dominant Position ◽  
Privacy Concerns ◽  
Sense Data ◽  
Abuse Of Dominant Position

Big data has a very important role in the digital economy, because firms have accurate tools to collect, store, analyse, treat, monetise and disseminate voluminous amounts of data. Companies have been improving their revenues with information about the behaviour, preferences, needs, expectations, desires and evaluations of their consumers. In this sense, data could be considered as a productive input. The article focuses on the current discussion regarding the possible use of competition law and policy to address privacy concerns related to big data companies. The most traditional and powerful tool to deal with privacy concerns is personal data protection law. Notwithstanding, the article examines whether competition law should play an important role in data-driven markets where privacy is a key factor. The article suggests a new approach to the following antitrust concepts in cases related to big data platforms: assessment of market power, merger notification thresholds, measurement of merger effects on consumer privacy, and investigation of abuse of dominant position. In this context, the article analyses decisions of competition agencies which reviewed mergers in big data-driven markets, such as Google/DoubleClick, Facebook/ WhatsApp and Microsoft/LinkedIn. It also reviews investigations of alleged abuse of dominant position associated with big data, in particular the proceeding opened by the Bundeskartellamt against Facebook, in which the German antitrust authority prohibited the data processing policy imposed by Facebook on its users. The article concludes that it is important to harmonise the enforcement of competition, consumer and data protection polices in order to choose the proper way to protect the users of dominant platforms, maximising the benefits of the data-driven economy.

scholarly journals Urban Crime and Security

2021 ◽  
pp. 213-228
Author(s):  
Tao Cheng ◽  
Tongxin Chen
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Crime Prevention ◽  
Urban Crime ◽  
Data Driven ◽  
Big Data Technologies ◽  
Urban Security ◽  
Made In

AbstractScientists have an enduring interest in understanding urban crime and developing security strategies for mitigating this problem. This chapter reviews the progress made in this topic from historic criminology to data-driven policing. It first reviews the broad implications of urban security and its implementation in practice. Next, it focuses on the tools to prevent urban crime and improve security, from analytical crime hotspot mapping to police resource allocation. Finally, a manifesto of data-driven policing is proposed, with its practical demand for efficient security strategies and the development of big data technologies. It emphasizes that data-driven strategies could be applied in cities due to their promising effectiveness for crime prevention and security improvement.

scholarly journals Healthcare Big Data and Personal Data Protection:

2016 ◽  
Vol 26 (1) ◽  
pp. 85-93
Author(s):  
Ryuichi Yamamoto
Keyword(s):  
Big Data ◽  
Data Protection ◽  
Personal Data ◽  
Personal Data Protection

Big Data: Technologies, Challenges, Data Analytics and Management: A Review

2017 ◽  
Vol 12 (01) ◽  
Author(s):  
Shweta Kaushik
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
High Speed ◽  
Information Base ◽  
Information Order ◽  
The World ◽  
Big Data Technologies ◽  
Business Profitability ◽  
The Web

Internet assumes an essential part in giving different learning sources to the world, which encourages numerous applications to give quality support of the customers. As the years go on the web is over-burden with parcel of data and it turns out to be difficult to extricate the applicable data from the web. This offers path to the advancement of the Big Data and the volume of the information continues expanding quickly step by step. Enormous Data has increased much consideration from the scholarly world and the IT business. In the advanced and figuring world, data is produced and gathered at a rate that quickly surpasses the limit go. Data mining procedures are utilized to locate the concealed data from the huge information. This Technique is utilized store, oversee, and investigate high speed of information and this information can be in any shape organized or unstructured frame. It is hard to handle substantial volume of information utilizing information base strategy like RDBMS. From one perspective, Big Data is amazingly important to deliver efficiency in organizations and transformative achievements in logical controls, which give us a considerable measure of chances to make incredible advances in many fields. There is most likely the future rivalries in business profitability and advances will without a doubt merge into the Big Data investigations. Then again, Big Data likewise emerges with many difficulties, for example, troubles in information catch, information stockpiling, information investigation and information perception. In this paper we concentrate on the audit of Big Data, its information order techniques and the way it can be mined utilizing different mining strategies.

scholarly journals OS BIG DATA E OS DADOS PESSOAIS ENTRE OS PRINCÍPIOS DA PROTEÇÃO E DA INOVAÇÃO

2020 ◽  
Vol 12 (1) ◽  
pp. 225-245
Author(s):  
Célia Zolynski
Keyword(s):  
Big Data ◽  
Latin American ◽  
Data Protection ◽  
Personal Data ◽  
European Union Law ◽  
General Data Protection Regulation ◽  
Personal Data Protection ◽  
Internet Regulation ◽  
New Type ◽  
General Data

Objective ”“ The article contrasts the problem of Big Data with the possibilities and limits of personal data protection. It is an original contribution to the academic discussion about the regulation of the Internet and the management of algorithms, focusing on Big Data. Methodology/approach/design ”“ The article provides bibliographic research on the opposition between Big Data and personal data protection, focusing on European Union law and French law. From the research is possible to identify regulatory alternatives do Big Data, whether legal-administrative nature or technological nature. Findings ”“ The article enlightens that, in addition to the traditional regulatory options, based on the law, there are technological options for regulating Big Data and algorithms. The article goes through an analysis of administrative performance, such as France’s CNIL (Commission nationale informatique et libertés, CNIL), to show that it has limits. Thus, the article concludes that there is a need to build a new type of regulation, one that is open to the inputs of regulated parties and civil society, in the form of new co-regulatory arrangements. Practical implications ”“ The article has an obvious application since the production of legal solutions for Internet regulation requires combining them with technological solutions. Brazil and several Latin American countries are experiencing this agenda, as they are building institutions and solutions to solve the dilemma of personal data protection. Originality/value ”“ The article clarifies several parts of the General Data Protection Regulation (EU Regulation 2016/679) and its applicability to Big Data. These new types of data processing impose several legal and regulatory challenges, whose solutions cannot be trivial and will rely on new theories and practices.

scholarly journals PROTECTION OF PRIVACY AND PERSONAL DATA IN THE BIG DATA ENVIRONMENT OF SMART CITIES

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 181-186
Author(s):  
A. Denker
Keyword(s):  
Big Data ◽  
Smart City ◽  
Personal Information ◽  
Smart Cities ◽  
Personal Data ◽  
Privacy And Security ◽  
Personal Data Protection ◽  
Security Issues ◽  
Data Environment ◽  
Bifocal Lenses

Abstract. The project of smart cities has emerged as a response to the challenges of twenty-first- century urbanization. Solutions to the fundamental conundrum of cities revolving around efficiency, convenience and security keep being sought by leveraging technology. Notwithstanding all the conveniences furnished by a smart city to all the citizens, privacy of a citizen is intertwined with the benefits of a smart city. The development processes which overlook privacy and security issues have left many of the smart city applications vulnerable to non-conventional security threats and susceptible to numerous privacy and personal data spillage risks. Among the challenges the smart city initiatives encounter, the emergence of the smartphone-big data-the cloud coalescence is perhaps the greatest, from the viewpoint of privacy and personal data protection. As our cities are getting digitalized, information comprising citizens' behavior, choices, and mobility, as well as their personal assets are shared over smartphone-big data-the cloud coalescences, thereby expanding cyber-threat surface and creating different security concerns. This coalescence refers to the practices of creating and analyzing vast sets of data, which comprise personal information. In this paper, the protection of privacy and personal data issues in the big data environment of smart cities are viewed through bifocal lenses, focusing on social and technical aspects. The protection of personal data and privacy in smart city enterprises is treated as a socio-technological operation where various actors and factors undertake different tasks. The article concludes by calling for novel developments, conceptual and practical changes both in technological and social realms.

scholarly journals Computational Simulation and Prediction on Electrical Conductivity of Oxide-Based Melts by Big Data Mining

Materials ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1059 ◽  
Author(s):  
Ao Huang ◽  
Yanzhu Huo ◽  
Juan Yang ◽  
Guangqiang Li
Keyword(s):  
Data Mining ◽  
Electrical Conductivity ◽  
Big Data ◽  
Molten Slag ◽  
Computational Simulation ◽  
Chemical Properties ◽  
Data Driven ◽  
Gradient Boosting ◽  
Big Data Mining ◽  
Physical Chemical

Electrical conductivity is one of the most basic physical–chemical properties of oxide-based melts and plays an important role in the materials and metallurgical industries. Especially with the metallurgical melt, molten slag, existing research studies related to slag conductivity mainly used traditional experimental measurement approaches. Meanwhile, the idea of data-driven decision making has been widely used in many fields instead of expert experience. Therefore, this study proposed an innovative approach based on big data mining methods to investigate the computational simulation and prediction of electrical conductivity. Specific mechanisms are discussed to explain the findings of our proposed approach. Experimental results show slag conductivity can be predicted through constructing predictive models, and the Gradient Boosting Decision Tree (GBDT) model is the best prediction model with 90% accuracy and more than 88% sensitivity. The robustness result of the GBDT model demonstrates the reliability of prediction outcomes. It is concluded that the conductivity of slag systems is mainly affected by TiO2, FeO, SiO2, and CaO. TiO2 and FeO are positively correlated with conductivity, while SiO2 and CaO have negative correlations with conductivity.

Data Mining Problems Classification and Techniques

2020 ◽  
pp. 70-93
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

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