scholarly journals Digital data: is a new legal regulation needed?

2021 ◽  
pp. 26-49
Author(s):  
Akvilė Medvedevaitė ◽  
Gabrielė Velta Mickevičiūtė
Keyword(s):  
Large Scale ◽  
Industrial Revolution ◽  
Rapid Development ◽  
Legal Regulation ◽  
Digital Data ◽  
Legal Issue ◽  
Daily Lives ◽  
Large Scale Data ◽  
Data Ownership ◽  
Data Subject

This article analyzes the phenomenon of digital data and its impact on both the daily lives of each individual and businesses. Article discusses the legal issue of data ownership, which is inextricably linked with the emergence of Big data. The EU legal regulation of digital data faces the following shortcomings: i. legal regulation of data does not keep pace with the rapid development of technology and the phenomenon of such large-scale data creation; ii. the current EU data legislation is intended to protect the interests of the data subject or business and not to create a common data regulatory ecosystem. For these reasons, the question of data ownership is raised, which is thought to be able to change the whole legal perception of digital data in the further evolution of the Industrial Revolution.

scholarly journals Basis of legal regulation and Internet censorship in China

2020 ◽  
pp. 1-9
Author(s):  
Do Lin'
Keyword(s):  
Information Technologies ◽  
Large Scale ◽  
Rapid Development ◽  
Political Stability ◽  
Legal Regulation ◽  
Chinese Government ◽  
User Data ◽  
Internet Censorship ◽  
The One ◽  
Automated Instruments

This article examines the basis of legal regulation and Internet censorship in China. The genesis, development and relevant regulatory basis of legal regulation of Internet in China is examined. The author comes to the conclusion that on the one hand, Internet in China is subject to tight control due to the rapid development of technologies of observation and increase of police access to user data. Currently, China is one of the leaders in engineering and export of automated instruments for monitoring social networks. The citizens face restrictions based on the control of login accounts that give access to the Internet; blockchain apps and their developers are also subject to control and must provide registration of real names of the users; international corporations, such as Apple, Microsoft, Linkedin, are forced to bend to the demands of Chinese authorities and help to determine and punish the users who do not adhere to the censorship requirements in China. On the other hand, Chinese government makes everything possible for the large scale implementation of information technologies into socioeconomic life of the country, namely industrial and commercial sectors. Usage of internet in the sphere of sociopolitical life restricted, since China justifiably sees a threat to political stability and social security of the country.

Efficient Indexing RDF Query Algorithm for Big Data

2013 ◽  
Vol 441 ◽  
pp. 691-694
Author(s):  
Yi Qun Zeng ◽  
Jing Bin Wang
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Large Data ◽  
Index Structure ◽  
Data Query ◽  
Large Scale Data ◽  
Tree Index ◽  
Rdf Data ◽  
Query Algorithm ◽  
Scale Data

With the rapid development of information technology, data grows explosionly, how to deal with the large scale data become more and more important. Based on the characteristics of RDF data, we propose to compress RDF data. We construct an index structure called PAR-Tree Index, then base on the MapReduce parallel computing framework and the PAR-Tree Index to execute the query. Experimental results show that the algorithm can improve the efficiency of large data query.

scholarly journals Trust in Centralized Large-Scale Data Repository: A Qualitative Analysis

2019 ◽  
Vol 15 (4) ◽  
pp. 365-378 ◽  
Author(s):  
Reinder Broekstra ◽  
Judith Aris-Meijer ◽  
Els Maeckelberghe ◽  
Ronald Stolk ◽  
Sabine Otten
Keyword(s):  
Large Scale ◽  
Personal Data ◽  
Digital Data ◽  
Data Repository ◽  
Key Factors ◽  
Human Research ◽  
The Public ◽  
Large Scale Data ◽  
Depth Interviews ◽  
Scale Data

Exponential increases in digital data and calls for participation in human research raise questions about when and why individuals voluntarily provide personal data. We conducted 36 in-depth interviews with ex-participants, participants, and nonparticipants in a biobank to identify key factors influencing trust in centralized large-scale data repository for human research. Our findings indicated that trust depends strongly on whether such data repository benefits the public, the interests of data collectors, the characteristics of the collected data, and application of informed consent for retaining control over personal data. Concerns about the aims and range of data repository appeared to influence withdrawal of participation. Our findings underscore ethical and practical issues relating to data collection and consent procedures in human research.

scholarly journals A Ranking-Based Hashing Algorithm Based on the Distributed Spark Platform

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 148
Author(s):  
Anbang Yang ◽  
Jiangbo Qian ◽  
Huahui Chen ◽  
Yihong Dong
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Modern Society ◽  
Training Time ◽  
Large Scale Data ◽  
Huge Data ◽  
Hashing Algorithm ◽  
Similarity Searches ◽  
Spark Framework ◽  
Scale Data

With the rapid development of modern society, generated data has increased exponentially. Finding required data from this huge data pool is an urgent problem that needs to be solved. Hashing technology is widely used in similarity searches of large-scale data. Among them, the ranking-based hashing algorithm has been widely studied due to its accuracy and speed regarding the search results. At present, most ranking-based hashing algorithms construct loss functions by comparing the rank consistency of data in Euclidean and Hamming spaces. However, most of them have high time complexity and long training times, meaning they cannot meet requirements. In order to solve these problems, this paper introduces a distributed Spark framework and implements the ranking-based hashing algorithm in a parallel environment on multiple machines. The experimental results show that the Spark-RLSH (Ranking Listwise Supervision Hashing) can greatly reduce the training time and improve the training efficiency compared with other ranking-based hashing algorithms.

Improvement of K-Means Algorithm for Accelerated Big Data Clustering

2021 ◽  
Vol 14 (2) ◽  
pp. 99-119
Author(s):  
Chunqiong Wu ◽  
Bingwen Yan ◽  
Rongrui Yu ◽  
Zhangshu Huang ◽  
Baoqin Yu ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Clustering ◽  
Large Scale ◽  
Rapid Development ◽  
Large Data ◽  
Data Retrieval ◽  
Research Directions ◽  
Large Scale Data ◽  
Rich Information ◽  
Scale Data

With the rapid development of the computer level, especially in recent years, “Internet +,” cloud platforms, etc. have been used in various industries, and various types of data have grown in large quantities. Behind these large amounts of data often contain very rich information, relying on traditional data retrieval and analysis methods, and data management models can no longer meet our needs for data acquisition and management. Therefore, data mining technology has become one of the solutions to how to quickly obtain useful information in today's society. Effectively processing large-scale data clustering is one of the important research directions in data mining. The k-means algorithm is the simplest and most basic method in processing large-scale data clustering. The k-means algorithm has the advantages of simple operation, fast speed, and good scalability in processing large data, but it also often exposes fatal defects in data processing. In view of some defects exposed by the traditional k-means algorithm, this paper mainly improves and analyzes from two aspects.

The Influence and Countermeasures Research of Large-Scale Distributed Photovoltaic Access to the Distribution Network

2014 ◽  
Vol 953-954 ◽  
pp. 61-65
Author(s):  
Jing Chao Zhang ◽  
Zheng Gang Wang ◽  
Feng Zhen Zhou ◽  
Ning Xi Song ◽  
Qian Wang
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Power Flow ◽  
Large Scale ◽  
Industrial Revolution ◽  
Simulation Analysis ◽  
Negative Impact ◽  
Rapid Development ◽  
Distribution Network ◽  
Photovoltaic Power

In recent years, with the gradual depletion of traditional energy, as renewable energy representatives, new energy has developed rapidly. We know that distributed photovoltaic power generation with clean, pollution-free, easy installation, and therefore has been rapid development. However, the large number of distributed photovoltaic power generation connected to the distribution network would have a negative impact on the grid with a safe and reliable operation because of its randomness and volatility intrinsic properties. In this paper, in terms of power flow, voltage distribution, load characteristics, power quality, system protection and reliability departure, through MATLAB simulation analysis, the distribution network transformation strategies of primary and secondary devices has been proposed. It laid an important foundation for renewable energy development and the Third Industrial Revolution.

scholarly journals PERAN PENDIDIKAN ISLAM DALAM MEMBENTUK KARAKTER SISWA DI ERA REVOLOSI INDUSTRI 4.0 DI MADRASAH TSANAWIYAH NEGERI 1 BANYUMAS

2019 ◽  
Vol 24 (2) ◽  
pp. 220-231
Author(s):  
Tutuk Ningsih
Keyword(s):  
Digital Literacy ◽  
Teaching And Learning ◽  
Industrial Revolution ◽  
Rapid Development ◽  
Islamic Education ◽  
Daily Lives ◽  
Extra Curricular Activities ◽  
Literacy Methods ◽  
History Of

  The role of Islamic education is a very strong foundation and becomes a reference of developing students’ character to face the Industrial Revolution 4.0 Era, and Islam is a source of truth and strength that can deliver ways on daily lives to achieve human character formation. Therefore, Islamic education is an important part of preparing the quality students. In other words, Islamic Education has a major contribution in the process of building students who have good character, have the ability to compete in the industrial revolution 4.0 era, have the ability to use global technology, and have the ability to adapt to the rapid development of technology. Islamic education is also expected to facilitate students to study hard consistently in order to face this era and students are expected to master sophisticated global technology without limits.    This study aims to describe and analyze the role of Islamic Education in building students’ character in the Industrial Revolution 4.0 era. To achieve the purposes of the study, this study used a qualitative method with a qualitative-naturalistic approach. Various data collection techniques used in this study are observation, in-depth interviews, and documentation. The results shows that the role of Islamic Education in building students' character was carried out through the following activities; 1) Intra-curricular activities: in this activity, the teacher inserts characters in the teaching and learning process of all subjects namely Qu'ran hadith, Fiqh, History of Islamic Culture, Arabic, and Aqeedah Akhlak. The teacher connects directly with material of akhlaq or character 2) Extra-curricular activities include: Tilawatil Qur'an (reading the Qur'an) using digital literacy methods, Tahfidzul Qur'an (memorizing the Qur'an), Qitobha, Hadroh (Islamic Music) and Calligraphy. Through these two types of activities built several characters: religious, honest, fond of reading, responsible, independent, appreciating achievements, caring socially and hard work. By having the character, the students are ready to face the industrial era 4.0.

Big data and extreme-scale computing

2018 ◽  
Vol 32 (4) ◽  
pp. 435-479 ◽  
Author(s):  
M Asch ◽  
T Moore ◽  
R Badia ◽  
M Beck ◽  
P Beckman ◽  
...  
Keyword(s):  
Big Data ◽  
High Performance ◽  
Large Scale ◽  
Digital Data ◽  
Distributed Sensors ◽  
Large Scale Data ◽  
Critical Problems ◽  
Extreme Scale ◽  
Shared Infrastructure ◽  
Large Scale Simulations

Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.

scholarly journals Massive Data Management and Sharing Module for Connectome Reconstruction

2020 ◽  
Vol 10 (5) ◽  
pp. 314
Author(s):  
Jingbin Yuan ◽  
Jing Zhang ◽  
Lijun Shen ◽  
Dandan Zhang ◽  
Wenhuan Yu ◽  
...  
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Large Scale ◽  
Rapid Development ◽  
Massive Data ◽  
Storage And Retrieval ◽  
Server Side ◽  
Large Scale Data ◽  
Client Side ◽  
Scale Data

Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can mine valuable information. For this purpose, we developed a data management module equipped with two parts, a storage and retrieval module on the server-side and an image cache module on the client-side. On the server-side, Hadoop and HBase are introduced to resolve massive data storage and retrieval. The pyramid model is adopted to store electron microscope images, which represent multiresolution data of the image. A block storage method is proposed to store volume segmentation results. We design a spatial location-based retrieval method for fast obtaining images and segments by layers rapidly, which achieves a constant time complexity. On the client-side, a three-level image cache module is designed to reduce latency when acquiring data. Through theoretical analysis and practical tests, our tool shows excellent real-time performance when handling large-scale data. Additionally, the server-side can be used as a backend of other similar software or a public database to manage shared datasets, showing strong scalability.

scholarly journals Assessment of the Awareness of Nigerian Professionals in the Built Environment on the Big Data analytics (BDA) Applications in the Construction Industry.

2021 ◽  
pp. 1-7
Author(s):  
Emmanuel Jesse Amadosi
Keyword(s):  
Big Data ◽  
Built Environment ◽  
Data Analytics ◽  
Large Scale ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Strong Relationship ◽  
Large Scale Data ◽  
Scale Data ◽  
Structured Questionnaire

With rapid development in technology, the built industry’s capacity to generate large-scale data is not in doubt. This trend of data upsurge labelled “Big Data” is currently being used to seek intelligent solutions in many industries including construction. As a result of this, the appeal to embrace Big Data Analytics has also gained wide advocacy globally. However, the general knowledge of Nigeria’s built environment professionals on Big Data Analytics is still limited and this gap continues to account for the slow pace of adoption of digital technologies like Big Data Analytics and the value it projects. This study set out to assess the level of awareness and knowledge of professionals within the Nigerian built environment with a view to promoting the adoption of Big Data Analytics for improved productivity. To achieve this aim, a structured questionnaire survey was carried out among a total of 283 professionals drawn from 9 disciplines within the built environment in the Federal Capital Territory, Abuja. The findings revealed that: a) a low knowledge level of Big Data exists among professionals, b) knowledge among professional and the level of Big Data Analytics application have strong relationship c) professional are interested in knowing more about the Big Data concept and how Big Data Analytics can be leveraged upon. The study, therefore recommends an urgent paradigm shift towards digitisation to fully embrace and adopt Big Data Analytics and enjoin stakeholders to promote collaborative schemes among practice-based professionals and the academia in seeking intelligent and smart solutions to construction-related problems.

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