Journal articles: 'Big data concepts'

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Riahi, Youssra, and Sara Riahi. "Big Data and Big Data Analytics: concepts, types and technologies." International Journal of Research and Engineering 5, no. 9 (November 2018): 524–28. http://dx.doi.org/10.21276/ijre.2018.5.9.5.

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Miloslavskaya, Natalia, and Alexander Tolstoy. "Big Data, Fast Data and Data Lake Concepts." Procedia Computer Science 88 (2016): 300–305. http://dx.doi.org/10.1016/j.procs.2016.07.439.

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Suvarnamukhi, B., and M. Seshashayee. "Big Data Concepts and Techniques in Data Processing." International Journal of Computer Sciences and Engineering 6, no. 10 (October 31, 2018): 712–14. http://dx.doi.org/10.26438/ijcse/v6i10.712714.

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Sánchez-Rada, Juan Fernando, Oscar Araque, Álvaro Carrera Barroso, and Carlos Ángel Iglesias Fernández. "Enseñando Big Data con Lápiz, Papel y Tijeras / Teaching Big Data With Pen, Paper and Scissors." Revista Internacional de Tecnologías en la Educación 5, no. 2 (January 25, 2019): 63–68. http://dx.doi.org/10.37467/gka-revedutech.v5.1794.

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ABSTRACTThis work proposesan approach that combines teaching general concepts in a technology-agnostic fashion with a cooperative learning approach oriented to a the resolution of a challenge in a competitive environment. In this way, students both learn the theory and then put in practice these concepts in class, exploring different options and cooperating in smalls groups. Such groups compete between them through in order to obtain the better solution. Our experience applying this approach in the classroom have been successful. Student satisfaction, test performance, and student understanding are high.RESUMENEste trabajo propone un enfoque al aprendizaje de Big Data, que combina los conceptos generales de una manera agnóstica a la tecnología, y la puesta en práctica de estos conceptos mediante aprendizaje cooperativo orientado a la resolución de un reto en un entorno competitivo. De esta manera, los alumnos aprenden los conceptos teóricos y los ponen en práctica explorando diferentes opciones y cooperando en grupos. Estos grupos compiten entre sí para obtener la mejor solución. Nuestra experiencia aplicando este enfoque ha sido un éxito.La satisfacción de los estudiantes, el rendimiento y la comprensión de los conceptos son altos.

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Kirillova, E. A. "Legal status and principles of using Big Data technology (Big Data)." Russian justice 2 (February 18, 2021): 68–69. http://dx.doi.org/10.18572/0131-6761-2021-2-68-69.

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The study analyzes the legal status and principles of Big Data technology, considers the role, features and significance of these technologies. The relevance of the research is dictated by the large-scale use of Big Data technologies in many areas and the weak legal regulation of the use of Big Data using personal data. The purpose of this study is to determine the legal status of Big Data technology and differentiate the concepts of ‘personal data’ and ‘Big Data technologies’. The study author’s definition of technology ‘Big Data’ and ‘personal data in electronic form’, developed principles for the use of Big Data technologies.

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Banumathi, S. "PREDICTIVE ANALYTICS CONCEPTS IN BIG DATA- A SURVEY." International Journal of Advanced Research in Computer Science 8, no. 8 (October 20, 2017): 27–30. http://dx.doi.org/10.26483/ijarcs.v8i8.4628.

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Figdor, Carrie. "Big Data and Changing Concepts of the Human." European Review 27, no. 3 (June 21, 2019): 328–40. http://dx.doi.org/10.1017/s1062798719000024.

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Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to non-human domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with non-humans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

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Tomić, Nenad, and Violeta Todorović. "The influence of Big data concept on future tendencies in payment systems." Megatrend revija 17, no. 3 (2020): 115–30. http://dx.doi.org/10.5937/megrev2003115t.

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The new wave of information and communication technology transformation relies on the concepts of the Internet of Things, Big Data and machine learning. These concepts will enable the connection and independent communication of a large number of devices, the processing of data that arises as a result of these processes and learning based on the refined information. Payment system is a sector that will experience major impacts by the coming changes. A large number of transactions create an information basis, whose analysis can provide precise inputs for business decision making. The subject of paper is the impact of managing a large amount of transactional data on key stakeholders in the payment process. The aim of the paper is to identify the key advantages and dangers that the Big Data concept will bring to the payment industry. The general conclusion is that the use of Big Data tools can facilitate the timely distribution of payment services and increase the security of transactions, but the price in the form of a loss of privacy is extremely high.

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Khine, Pwint Phyu, and Zhao Shun Wang. "Data lake: a new ideology in big data era." ITM Web of Conferences 17 (2018): 03025. http://dx.doi.org/10.1051/itmconf/20181703025.

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Data Lake is one of the arguable concepts appeared in the era of big data. Data Lake original idea is originated from business field instead of academic field. As Data Lake is a newly conceived idea with revolutionized concepts, it brings many challenges for its adoption. However, the potential to change the data landscape makes the research of Data Lake worthwhile.

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Hassani, Hossein, Xu Huang, and Emmanuel Silva. "Big-Crypto: Big Data, Blockchain and Cryptocurrency." Big Data and Cognitive Computing 2, no. 4 (October 19, 2018): 34. http://dx.doi.org/10.3390/bdcc2040034.

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Cryptocurrency has been a trending topic over the past decade, pooling tremendous technological power and attracting investments valued over trillions of dollars on a global scale. The cryptocurrency technology and its network have been endowed with many superior features due to its unique architecture, which also determined its worldwide efficiency, applicability and data intensive characteristics. This paper introduces and summarises the interactions between two significant concepts in the digitalized world, i.e., cryptocurrency and Big Data. Both subjects are at the forefront of technological research, and this paper focuses on their convergence and comprehensively reviews the very recent applications and developments after 2016. Accordingly, we aim to present a systematic review of the interactions between Big Data and cryptocurrency and serve as the one stop reference directory for researchers with regard to identifying research gaps and directing future explorations.

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Belov, Sergey, Daria Zrelova, and Vladimir Korenkov. "Big Data and digital economy." System Analysis in Science and Education, no. 2 (2020) (June 30, 2020): 187–97. http://dx.doi.org/10.37005/2071-9612-2020-2-187-197.

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In this paper, Big Data is considered as an "umbrella" term that combines various concepts, technologies and methods of data processing in distributed information systems that provide a qualitatively new useful information (new knowledge). The stages of "standard" research in the Big Data approach are described. A brief description of the Big Data ecosystem, which consists of several main categories, is given. Various projects and initiatives at the national and international levels are considered, as well as examples of the use of Big Data in business, economy, and society. As concrete examples of the construction and use of analytical platforms for Big Data, successful socio-economic research carried out by the authors as part of research teams at the Plekhanov Russian University of Economics is presented. The Big data metaphor is certainly successful, since it naturally connects a complex of concepts, technologies and methods of Big data with the economy by hinting at a connection with other well-known metaphors –"Big oil", "Big ore", etc.

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Liu, Che-Hung, Jen Sheng Wang, and Ching-Wei Lin. "The concepts of big data applied in personal knowledge management." Journal of Knowledge Management 21, no. 1 (February 13, 2017): 213–30. http://dx.doi.org/10.1108/jkm-07-2015-0298.

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Purpose The purpose of this paper is to demonstrate the applications of big data in personal knowledge management (PKM). Design/methodology/approach Five conventional knowledge management dimensions, namely, the value of data, data collection, data storage, data application and data presentation, were applied for integrating big data in the context of PKM. Findings This study concludes that time management, computer usage efficiency management, mobile device usage behavior management, health management and browser surfing management are areas where big data can be applied to PKM. Originality/value While the literature discusses PKM without considering the impact of big data, this paper aims to extend existing knowledge by demonstrating the application of big data in PKM.

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Krimpmann, Dominik, and Anna Stühmeier. "Big Data and Analytics." International Journal of Service Science, Management, Engineering, and Technology 8, no. 3 (July 2017): 79–92. http://dx.doi.org/10.4018/ijssmet.2017070105.

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Big Data and Analytics have become key concepts within the corporate world, both commercially and from an information technology (IT) perspective. This paper presents the results of a global quantitative analysis of 400 IT leaders from different industries, which examined their attitudes toward dedicated roles for an Information Architect and a Data Scientist. The results illustrate the importance of these roles at the intersection of business and technology. They also show that to build sustainable and quantifiable business results and define an organization's competitive positioning, both roles need to be dedicated, rather than shared across different people. The research also showed that those dedicated roles contribute actively to a sustainable competitive positioning mainly driven by visualization of complex matters.

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Brady, Henry E. "The Challenge of Big Data and Data Science." Annual Review of Political Science 22, no. 1 (May 11, 2019): 297–323. http://dx.doi.org/10.1146/annurev-polisci-090216-023229.

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Big data and data science are transforming the world in ways that spawn new concerns for social scientists, such as the impacts of the internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber-warfare and cyber-terrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation. Along with these changes in society, powerful new data science methods support research using administrative, internet, textual, and sensor-audio-video data. Burgeoning data and innovative methods facilitate answering previously hard-to-tackle questions about society by offering new ways to form concepts from data, to do descriptive inference, to make causal inferences, and to generate predictions. They also pose challenges as social scientists must grasp the meaning of concepts and predictions generated by convoluted algorithms, weigh the relative value of prediction versus causal inference, and cope with ethical challenges as their methods, such as algorithms for mobilizing voters or determining bail, are adopted by policy makers.

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Venkatram, Kari, and Mary A. Geetha. "Review on Big Data & Analytics – Concepts, Philosophy, Process and Applications." Cybernetics and Information Technologies 17, no. 2 (June 1, 2017): 3–27. http://dx.doi.org/10.1515/cait-2017-0013.

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Abstract Big Data analytics has been the main focus in all the industries today. It is not overstating that if an enterprise is not using Big Data analytics, it will be a stray and incompetent in their businesses against their Big Data enabled competitors. Big Data analytics enables business to take proactive measure and create a competitive edge in their industry by highlighting the business insights from the past data and trends. The main aim of this review article is to quickly view the cutting-edge and state of art work being done in Big Data analytics area by different industries. Since there is an overwhelming interest from many of the academicians, researchers and practitioners, this review would quickly refresh and emphasize on how Big Data analytics can be adopted with available technologies, frameworks, methods and models to exploit the value of Big Data analytics.

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Prasdika, Prasdika, and Bambang Sugiantoro. "A Review Paper on Big Data and Data Mining Concepts and Techniques." IJID (International Journal on Informatics for Development) 7, no. 1 (December 4, 2018): 33. http://dx.doi.org/10.14421/ijid.2018.07107.

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In the digital era like today the growth of data in the database is very rapid, all things related to technology have a large contribution to data growth as well as social media, financial technology and scientific data. Therefore, topics such as big data and data mining are topics that are often discussed. Data mining is a method of extracting information through from big data to produce an information pattern or data anomaly

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Gandomi, Amir, and Murtaza Haider. "Beyond the hype: Big data concepts, methods, and analytics." International Journal of Information Management 35, no. 2 (April 2015): 137–44. http://dx.doi.org/10.1016/j.ijinfomgt.2014.10.007.

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Ostrowski, David Alfred. "Big Data: A Strategic Perspective." International Journal of Semantic Computing 08, no. 03 (September 2014): 319–33. http://dx.doi.org/10.1142/s1793351x1440011x.

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Big Data has become ubiquitous across all areas of research allowing for new applications that were not possible earlier. Unlike software development relying on traditional data sources, Big Data applications present their own unique challenges to appropriately harness the utility of the Apache Hadoop architecture. In this paper, we introduce fundamental concepts of Hadoop and explore its usage as well as future direction. We also present our strategy for exploring the Hadoop architecture including addressing issues of scalability, customization of code and utilization of programming techniques.

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Mishra, Deepa, Zongwei Luo, Shan Jiang, Thanos Papadopoulos, and Rameshwar Dubey. "A bibliographic study on big data: concepts, trends and challenges." Business Process Management Journal 23, no. 3 (June 5, 2017): 555–73. http://dx.doi.org/10.1108/bpmj-10-2015-0149.

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Purpose The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field. Design/methodology/approach To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals. Findings The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data. Research limitations/implications This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research. Originality/value To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

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Sadiku, Matthew, Justin Foreman, and Sarhan Musa. "BIG DATA ANALYTICS: A PRIMER." International Journal of Engineering Technologies and Management Research 5, no. 9 (March 21, 2020): 44–49. http://dx.doi.org/10.29121/ijetmr.v5.i9.2018.287.

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The use of digital devices and systems such smart phones, computers, the Internet, and social media has resulted in a massive volume of data which is exponentially increasing daily. Such data is processed using multiple techniques, collectively known as big data analytics. Big data analytics is the process of examining large amounts of data (big data) to uncover hidden patterns, correlations, and other insights. Analyzing big data enables organizations and businesses to make better and faster decisions. This paper briefly presents the fundamental concepts of big data analytics and its tools.

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Savoska, Snezana, and Blagoj Ristevski. "Towards Implementation of Big Data Concepts in a Pharmaceutical Company." Open Computer Science 10, no. 1 (October 27, 2020): 343–56. http://dx.doi.org/10.1515/comp-2020-0201.

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AbstractNowadays, big data is a widely utilized concept that has been spreading quickly in almost every domain. For pharmaceutical companies, using this concept is a challenging task because of the permanent pressure and business demands created through the legal requirements, research demands and standardization that have to be adopted. These legal and standards’ demands are associated with human healthcare safety and drug control that demands continuous and deep data analysis. Companies update their procedures to the particular laws, standards, market demands and regulations all the time by using contemporary information technology. This paper highlights some important aspects of the experience and change methodology used in one Macedonian pharmaceutical company, which has employed information technology solutions that successfully tackle legal and business pressures when dealing with a large amount of data. We used a holistic view and deliverables analysis methodology to gain top-down insights into the possibilities of big data analytics. Also, structured interviews with the company’s managers were used for information collection and proactive methodology with workshops was used in data integration toward the implementation of big data concepts. The paper emphasizes the information and knowledge used in this domain to improve awareness for the needs of big data analysis to achieve a competitive advantage. The main results are focused on systematizing the whole company’s data, information and knowledge and propose a solution that integrates big data to support managers’ decision-making processes.

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Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.

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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.

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Zeng, Marcia Lei. "Smart Data for Digital Humanities." Journal of Data and Information Science 2, no. 1 (February 18, 2017): 1–12. http://dx.doi.org/10.1515/jdis-2017-0001.

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AbstractThe emergence of “Big Data” has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being—“Smart Data.” This paper shares the author’s understanding ofwhat,why,how,who,where, andwhich datain relation to Smart Data and digital humanities. It concludes that, challenges and opportunities co-exist, but it is certain that Smart Data, the ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, will continue to have extraordinary value in digital humanities.The emergence of “Big Data” has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being—“Smart Data.”

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Shalini, M. "A Survey of Big Data – Challenges, on Characteristics and Concepts." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (January 31, 2018): 937–40. http://dx.doi.org/10.22214/ijraset.2018.1141.

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S. M, Kawale, Holambe A. N, and Bokefode J. D. "A Review on Big Data Concepts and various Analytic Techniques." International Journal of Computer Trends and Technology 52, no. 1 (October 25, 2017): 13–16. http://dx.doi.org/10.14445/22312803/ijctt-v52p104.

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Lim, Michele C., and Gary N. Holland. "Clinical Concepts, Coding, and How It Relates to Big Data." JAMA Ophthalmology 136, no. 10 (October 1, 2018): 1191. http://dx.doi.org/10.1001/jamaophthalmol.2018.2998.

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Dahlstedt, Palle. "Big Data and Creativity." European Review 27, no. 3 (July 2019): 411–39. http://dx.doi.org/10.1017/s1062798719000073.

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Big data and machine learning techniques are increasingly applied to creative tasks, often with strong reactions of both awe and concern. But we have to be careful about where to attribute the creative agency. Is it really the machine that paints like van Gogh, or is it a human that uses a high-level tool to impart one pattern upon another, based on her aesthetic preferences? In this paper, the author analyses the problem of machine creativity, focusing on four central themes: the inherent convergence of machine learning and big data techniques, their dependence on assumptions and incomplete data, the possibility of explorative search as a new creative paradigm, and the related problem of the opacity of results from such methods. The Google Deep Dream project is brought in as an example to illustrate the discussion. Information and complexity are brought into the discussion as central concepts for both creative processes and the resulting artefacts, concluding that the complexity of the interaction between the creative agent and the environment during the creative process is a crucial parameter for meaningful creative output. Based on the exposed limitations in current technologies, the author concludes that the principal creative agency still lies in the developers and users of the tools, not in the data processing itself. Human effort and input still matters. But we can take a constructive approach, regarding big data techniques as tools one order of magnitude more complex than what was available before, allowing artists to work with abstractions previously unfeasible for computational work.

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Long, Cu Kim, Rashmi Agrawal, Ha Quoc Trung, and Hai Van Pham. "A big data framework for E-Government in Industry 4.0." Open Computer Science 11, no. 1 (January 1, 2021): 461–79. http://dx.doi.org/10.1515/comp-2020-0191.

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Abstract The next generation of E-Government and healthcare has the potential to increase the more intelligent governance with improvements in transparency, accountability, efficiency, and effectiveness. It enables organizations to use the benefits of information via big data analysis to settle the difficulties effectively. Big Data has emerged which plays a significant role in many sectors around the world. Global trends in taking advantage of the benefits from big data are considered with an overview of the US, European Union, and several developing countries. To deeply understand the utilization of big data in several domains, this study has presented a brief survey of key concepts (such as IoT-enabled data, blockchain-enabled data, and intelligent systems data) to deeply understand the utilization of big data in several domains. Our analysis sets out also the similarities and differences in these concepts. We have also surveyed state-of-the-art technologies including cloud computing, multi-cloud, webservice, and microservice which are used to exploit potential benefits of big data analytics. Furthermore, some typical big data frameworks are surveyed and a big data framework for E-Government is also proposed. Open research questions and challenges are highlighted (for researchers and developers) following our review. Our goal in presenting the novel concepts presented in this article is to promote creative ideas in the research endeavor to perform efficaciously next-generation E-Government in the context of Industry 4.0.

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Jovanovic Milenkovic, Marina, Aleksandra Vukmirovic, and Dejan Milenkovic. "Big data analytics in the health sector: challenges and potentials." Management:Journal of Sustainable Business and Management Solutions in Emerging Economies 24, no. 1 (March 19, 2019): 23. http://dx.doi.org/10.7595/management.fon.2019.0001.

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Research Question: The introduction of the Big Data concept in the healthcare sector points to a major challenge and potential. Motivation: Our goal is to indicate the importance of analyzing and processing large amounts of data that go beyond the typical ways of storing and processing information. Тhе data have their own characteristics: volume, velocity and variety. There are different structures. Analysis of these data is possible with the Big Data concept. Its importance is most evident in the health sector, because the preservation of the health status of the population depends on adequate data analysis. Idea: The idea of the paper is that big health data analytics contributes to a better quality provision of health services. The process is more efficient and effective. Data: Health analytics suggests that more and more resources are being utilized globally. In order to achieve improvements, health analytics and Big data concepts play a vital role in overcoming the obstacles, working more efficiently and aiming at providing adequate medical care. Tools: The Big data concept will help identify patients with developed chronic diseases. Big data can identify outbreaks of flu or other epidemics in real time. In this way, they are managed by the healthcare system, reducing overall healthcare costs over time, and increasing revenues. Findings: A key policy challenge is to improve the outcomes of the healthcare system, data collection and analysis, security, storage and transfers. Big data are the potential to improve quality of care, improve predictions of diseases, improve the treatment methods, reduce costs. Contribution: This paper points to the challenges and potentials of Big Health Data analytics and formulates good reasons to apply the Big Data concept in healthcare.

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Ibrahim, Nadia, Alaa Hassan, and Marwah Nihad. "Big Data Analysis of Web Data Extraction." International Journal of Engineering & Technology 7, no. 4.37 (December 13, 2018): 168. http://dx.doi.org/10.14419/ijet.v7i4.37.24095.

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In this study, the large data extraction techniques; include detection of patterns and secret relationships between factors numbering and bring in the required information. Rapid analysis of massive data can lead to innovation and concepts of the theoretical value. Compared with results from mining between traditional data sets and the vast amount of large heterogeneous data interdependent it has the ability expand the knowledge and ideas about the target domain. We studied in this research data mining on the Internet. The various networks that are used to extract data onto different locations complex may appear sometimes and has been used to extract information on the web technology to extract and data analysis (Marwah et al., 2016). In this research, we extracted the information on large quantities of the web pages and examined the pages of the site using Java code, and we added the extracted information on a special database for the web page. We used the data network function to get accurate results of evaluating and categorizing the data pages found, which identifies the trusted web or risky web pages, and imported the data onto a CSV extension. Consequently, examine and categorize these data using WEKA to obtain accurate results. We concluded from the results that the applied data mining algorithms are better than other techniques in classification and extraction of data and high performance.

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Farkowski, Michał M., and Filip Morawski. "Big data and atrial fibrillation – where we are?" In a good rythm 3, no. 56 (October 21, 2020): 27–29. http://dx.doi.org/10.5604/01.3001.0014.4643.

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The term Big data defines set of data that is characterized by its volume, velocity and variety. The authors present basic concepts of Big Data acquisition and analysis together with contemporary examples of its utilization in diagnosis and treatment of atrial fibrillation.

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Seitkulov, Yerzhan N., Seilkhan N. Boranbayev, Gulden B. Ulyukova, Banu B. Yergaliyeva, and Dina Satybaldina. "Methods for secure cloud processing of big data." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (June 1, 2021): 1650. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1650-1658.

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We study new methods of secure cloud processing of big data when solving applied computationally-complex problems with secret parameters. This is one of the topical issues of secure client-server communication. As part of our research work, we model the client-server interactions: we give specific definitions of such concepts as “solvable by the protocol”, “secure protocol”, “correct protocol”, as well as actualize the well-known concepts-“active attacks” and “passive attacks”. First, we will outline the theory and methods of secure outsourcing for various abstract equations with secret parameters, and then present the results of using these methods in solving applied problems with secret parameters, arising from the modeling of economic processes. Many economic tasks involve processing a large set of economic indicators. Therefore, we are considering a typical economic problem that can only be solved on very powerful computers.

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Li, Lan. "Analysis and Research of Problems Faced by Big Data Era." Applied Mechanics and Materials 347-350 (August 2013): 3252–56. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3252.

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In this paper, the basic concepts of Big Data, as well as problems faced by Big Data research and corresponding research results were introduced. This research intends to provide references for the rapid processing of complex data and effective acquisition of useful information in Big Data Era.

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Bellocchio, Lucía. "Big Data in the public sector." A&C - Revista de Direito Administrativo & Constitucional 18, no. 72 (April 1, 2018): 13–29. http://dx.doi.org/10.21056/aec.v18i72.967.

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There is no doubt that one of the most obvious and far-reaching derivations of the Internet and global interconnection through the network is the enormous volume of information to which we have access. It is in this context that the so-called "Big Data" appears, exposing us to great changes in the different areas of our lives, proposing scenarios that point to open governments, transparency and greater closeness to citizens. However, there are many challenges that this new reality poses on Public Administration and there appears not to be unique strategies or models for its implementation. The aim of this work is to review some of the most important concepts that are involved in this era of Big Data in the public sector.

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Shi, Peng, Yulin Cui, Kangming Xu, Mingmei Zhang, and Lianhong Ding. "Data Consistency Theory and Case Study for Scientific Big Data." Information 10, no. 4 (April 12, 2019): 137. http://dx.doi.org/10.3390/info10040137.

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Big data technique is a series of novel technologies to deal with large amounts of data from various sources. Unfortunately, it is inevitable that the data from different sources conflict with each other from the aspects of format, semantics, and value. To solve the problem of conflicts, the paper proposes data consistency theory for scientific big data, including the basic concepts, properties, and quantitative evaluation method. Data consistency can be divided into different grades as complete consistency, strong consistency, weak consistency, and conditional consistency according to consistency degree and application demand. The case study is executed on material creep testing data. The analysis results show that the theory can solve the problem of conflicts in scientific big data.

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Hwang, Hae-Ik, Sun-Hyoung Kim, and Kang-Hoon Lee. "A Network Analysis of Mothers’ Happiness Concepts based on Big Data." Journal of Future Early Childhood Education 25, no. 2 (May 25, 2018): 45–66. http://dx.doi.org/10.22155/jfece.25.2.45.66.

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Babanli, M. B. "Synthesis of new materials by using fuzzy and big data concepts." Procedia Computer Science 120 (2017): 104–11. http://dx.doi.org/10.1016/j.procs.2017.11.216.

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Srinivasulu, Balu, and Andemariam Mebrahtu. "Concepts and Technologies of Big Data Management and Hadoop File System." International Journal of Computer Trends and Technology 44, no. 2 (February 25, 2017): 80–88. http://dx.doi.org/10.14445/22312803/ijctt-v44p114.

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Matney, Susan A., Theresa (Tess) Settergren, Jane M. Carrington, Rachel L. Richesson, Amy Sheide, and Bonnie L. Westra. "Standardizing Physiologic Assessment Data to Enable Big Data Analytics." Western Journal of Nursing Research 39, no. 1 (July 21, 2016): 63–77. http://dx.doi.org/10.1177/0193945916659471.

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Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.

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Thiessard, F., and V. Koutkias. "Big Data - Smart Health Strategies." Yearbook of Medical Informatics 23, no. 01 (August 2014): 48–51. http://dx.doi.org/10.15265/iy-2014-0031.

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Summary Objectives: To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts. Methods: A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field. Results: The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs. Conclusions: The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future.

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Heinrich, Falk. "(Big) Data, Diagram Aesthetics and the Question concerning Beauty." MedieKultur: Journal of media and communication research 31, no. 59 (March 8, 2016): 20. http://dx.doi.org/10.7146/mediekultur.v31i59.20084.

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<p class="falk1">The article investigates whether and in which way artistic artefacts deploying big data can be experienced as beautiful. The question is relevant, because the sentiment of beauty indicates besides an immediate sensory valuation also changes in cultural values and epistemic frameworks. The article focuses on artistic data visualisations. It applies concepts of philosophical aesthetics in order to trace an altered notion of beauty and its artistic and cultural implications.</p><p class="falk1">The article’s introductory part presents some examples of data visualisations and introduces relevant notions of beauty and big data. The main part discusses the changes in our concept of beauty by analysing data visualisation in the light of conceptual art and its aesthetics of the sublime. Data visualizations present potentially unfathomable and complex information that is associated with the sublime, but represent data in a way that allows for understanding by means of imaginations, which are aspects of beauty. The article elaborates on the simultaneity of and oscillation between aesthetic beauty and the aesthetic sublime, by introducing Deleuze’s understandings of the concept of the diagram that is able to mediate between visualisation as representation and diagrams as performative machine of formation and displacement of data relations.</p>

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Matzner, Tobias. "Why privacy is not enough privacy in the context of “ubiquitous computing” and “big data”." Journal of Information, Communication and Ethics in Society 12, no. 2 (May 6, 2014): 93–106. http://dx.doi.org/10.1108/jices-08-2013-0030.

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Purpose – Ubiquitous computing and “big data” have been widely recognized as requiring new concepts of privacy and new mechanisms to protect it. While improved concepts of privacy have been suggested, the paper aims to argue that people acting in full conformity to those privacy norms still can infringe the privacy of others in the context of ubiquitous computing and “big data”. Design/methodology/approach – New threats to privacy are described. Helen Nissenbaum's concept of “privacy as contextual integrity” is reviewed concerning its capability to grasp these problems. The argument is based on the assumption that the technologies work, persons are fully informed and capable of deciding according to advanced privacy considerations. Findings – Big data and ubiquitous computing enable privacy threats for persons whose data are only indirectly involved and even for persons about whom no data have been collected and processed. Those new problems are intrinsic to the functionality of these new technologies and need to be addressed on a social and political level. Furthermore, a concept of data minimization in terms of the quality of the data is proposed. Originality/value – The use of personal data as a threat to the privacy of others is established. This new perspective is used to reassess and recontextualize Helen Nissenbaum's concept of privacy. Data minimization in terms of quality of data is proposed as a new concept.

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Lasso Cardona, Luis Adrián. "Big data, key factor for the knowledge society." Respuestas 24, no. 3 (September 1, 2019): 39–53. http://dx.doi.org/10.22463/0122820x.1848.

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We are currently in an era of information explosion that affects our life in one way or another. Because of this, the transformation of huge databases into knowledge has become one of the tasks of greatest interest to society in general. Big Data was born as an instrument for knowledge due to the inability of current computer systems to store and process large volumes of data. The knowledge society arises from the use of technologies such as Big Data. The purpose of this article is to analyze the influence of Big Data on the knowledge society through a review of the state of the art supported by research articles and books published in the last 15 years, which allow us to put these two terms into context, understand their relationship and highlight the influence of Big Data as a generator of knowledge for today's society. The concept of Big Data, and its main applications to society will be defined. The concept of the Information Society is addressed and the main challenges it has are established. The relationship between both concepts is determined. And finally the conclusions are established. In order to reduce the digital divide, it is imperative to make profound long-term changes in educational models and public policies on investment, technology and employment that allow the inclusion of all social classes. In this sense, knowledge societies with the help of Big Data are called to be integrative elements and transform the way they are taught and learned, the way they are investigated, new social and economic scenarios are simulated, the brand decisions in Companies and share knowledge.

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Abidi, Syed Sibte Raza, and Samina Raza Abidi. "Intelligent health data analytics: A convergence of artificial intelligence and big data." Healthcare Management Forum 32, no. 4 (May 22, 2019): 178–82. http://dx.doi.org/10.1177/0840470419846134.

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Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.

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Liu, Zhenghao, and Xi Zeng. "Hybrid Intelligence in Big Data Environment: Concepts, Architectures, and Applications of Intelligent Service." Data and Information Management 5, no. 2 (January 5, 2021): 262–76. http://dx.doi.org/10.2478/dim-2020-0051.

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Abstract Based on the emerging concept of “Hybrid Intelligence,” this paper aims to explore a new model of human–computer interaction, and deeply research on its development and application of Intelligent Service in the big data environment. It systematically explores the related academic concepts of hybrid intelligence, and establishes its architecture model. The development of hybrid intelligence is faced with cognitive differences, system fragmentation, human–machine digital divide, and other issues. Strengthening the interaction between cognition and perception can be the key to break through the bottleneck. The intelligent service system based on the hybrid intelligent architecture takes knowledge fusion as the core, and “cloud intelligent brain” is making it possible for the human–computer symbiosis driven by hybrid intelligence. The proposed advanced human–computer interaction mode constructs a hybrid intelligent architecture model, enriches the concept system of human–machine hybrid intelligence, and provides a new landing scheme for intelligent services based on complex scenes in the big data environment.

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El-Seoud, Samir Abou, Hosam F. El-Sofany, Mohamed Ashraf Fouad Abdelfattah, and Reham Mohamed. "Big Data and Cloud Computing: Trends and Challenges." International Journal of Interactive Mobile Technologies (iJIM) 11, no. 2 (April 11, 2017): 34. http://dx.doi.org/10.3991/ijim.v11i2.6561.

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Big data is currently one of the most critical emerging technologies. Big Data are used as a concept that refers to the inability of traditional data architectures to efficiently handle the new data sets. The 4V’s of big data – volume, velocity, variety and veracity makes the data management and analytics challenging for the traditional data warehouses. It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and selfsufficient. The integrating big data with cloud computing technologies, businesses and education institutes can have a better direction to the future. The capability to store large amounts of data in different forms and process it all at very large speeds will result in data that can guide businesses and education institutes in developing fast. Nevertheless, there is a large concern regarding privacy and security issues when moving to the cloud which is the main causes as to why businesses and educational institutes will not move to the cloud. This paper introduces the characteristics, trends and challenges of big data. In addition to that, it investigates the benefits and the risks that may rise out of the integration between big data and cloud computing.

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Yan, Yilin, Mei-Ling Shyu, and Qiusha Zhu. "Supporting Semantic Concept Retrieval with Negative Correlations in a Multimedia Big Data Mining System." International Journal of Semantic Computing 10, no. 02 (June 2016): 247–67. http://dx.doi.org/10.1142/s1793351x16400092.

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With the extensive use of smart devices and blooming popularity of social media websites such as Flickr, YouTube, Twitter, and Facebook, we have witnessed an explosion of multimedia data. The amount of data nowadays is formidable without effective big data technologies. It is well-acknowledged that multimedia high-level semantic concept mining and retrieval has become an important research topic; while the semantic gap (i.e., the gap between the low-level features and high-level concepts) makes it even more challenging. To address these challenges, it requires the joint research efforts from both big data mining and multimedia areas. In particular, the correlations among the classes can provide important context cues to help bridge the semantic gap. However, correlation discovery is computationally expensive due to the huge amount of data. In this paper, a novel multimedia big data mining system based on the MapReduce framework is proposed to discover negative correlations for semantic concept mining and retrieval. Furthermore, the proposed multimedia big data mining system consists of a big data processing platform with Mesos for efficient resource management and with Cassandra for handling data across multiple data centers. Experimental results on the TRECVID benchmark datasets demonstrate the feasibility and the effectiveness of the proposed multimedia big data mining system with negative correlation discovery for semantic concept mining and retrieval.

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Jin, Dong-Hui, and Hyun-Jung Kim. "Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics." Sustainability 10, no. 10 (October 19, 2018): 3778. http://dx.doi.org/10.3390/su10103778.

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Efficient decision making based on business intelligence (BI) is essential to ensure competitiveness for sustainable growth. The rapid development of information and communication technology has made collection and analysis of big data essential, resulting in a considerable increase in academic studies on big data and big data analysis (BDA). However, many of these studies are not linked to BI, as companies do not understand and utilize the concepts in an integrated way. Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data, and BDA to show that they are not separate methods but an integrated decision support system. Second, we explore how businesses use big data and BDA practically in conjunction with BI through a case study of sorting and logistics processing of a typical courier enterprise. We focus on the company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from actual application. Our findings may enable companies to achieve management efficiency by utilizing big data through efficient BI without investing in additional infrastructure. It could also give them indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.

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Raevich, Aleksey, Boris Dobronets, Olga Popova, and Ksenia Raevich. "Conceptual model of operational–analytical data marts for big data processing." E3S Web of Conferences 149 (2020): 02011. http://dx.doi.org/10.1051/e3sconf/202014902011.

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Operational data marts that basically constitute slices of thematic narrowly-focused information are designed to provide operational access to big data sources due to consolidation and ranking of information resources based on their relevance. Unlike operational data marts dependent on the sources, analytical data marts are considered as independent data sources created by users to provide structuring of data for the tasks being solved. Therefore, the conceptual model of operational-analytical data marts allows combining the concepts of operational and analytical data marts to generate an analytical cluster that shall act as the basis for quick designing, development and implementation of data models.

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Thirunarayan, Krishnaprasad, and Amit Sheth. "Semantics-Empowered Big Data Processing with Applications." AI Magazine 36, no. 1 (March 25, 2015): 39–54. http://dx.doi.org/10.1609/aimag.v36i1.2566.

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We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical-cyber-social systems. To handle volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle veracity, we explore trust models and approaches to glean trustworthiness. These four v's of big data are harnessed by the semantics-empowered analytics to derive value to support applications transcending physical-cyber-social continuum.

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Journal articles on the topic 'Big data concepts'

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