Relevant bibliographies by topics / Big data concepts

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Big data concepts'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Big data concepts"

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Islam, Md Zahidul. "A Cloud Based Platform for Big Data Science." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103700.

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With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large data sets, collectively known as “big data”. The term “big-data” in this thesis refers to large, diverse, complex, longitudinal and/or distributed data sets generated from instruments, sensors, internet transactions, email, social networks, twitter streams, and/or all digital sources available today and in the future. We introduced architectures and concepts for implementing a cloud-based infrastructure for analyzing large volume of semi-structured and unstructured data. We built and evaluated an application prototype for collecting, organizing, processing, visualizing and analyzing data from the retail industry gathered from indoor navigation systems and social networks (Twitter, Facebook etc). Our finding was that developing large scale data analysis platform is often quite complex when there is an expectation that the processed data will grow continuously in future. The architecture varies depend on requirements. If we want to make a data warehouse and analyze the data afterwards (batch processing) the best choices will be Hadoop clusters and Pig or Hive. This architecture has been proven in Facebook and Yahoo for years. On the other hand, if the application involves real-time data analytics then the recommendation will be Hadoop clusters with Storm which has been successfully used in Twitter. After evaluating the developed prototype we introduced a new architecture which will be able to handle large scale batch and real-time data. We also proposed an upgrade of the existing prototype to handle real-time indoor navigation data.
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Bockermann, Christian [Verfasser], Katharina [Akademischer Betreuer] Morik, and Albert [Gutachter] Bifet. "Mining big data streams for multiple concepts / Christian Bockermann. Betreuer: Katharina Morik. Gutachter: Albert Bifet." Dortmund : Universitätsbibliothek Dortmund, 2015. http://d-nb.info/1111103259/34.

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Risch, Jean-Charles. "Enrichissement des Modèles de Classification de Textes Représentés par des Concepts." Thesis, Reims, 2017. http://www.theses.fr/2017REIMS012/document.

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La majorité des méthodes de classification de textes utilisent le paradigme du sac de mots pour représenter les textes. Pourtant cette technique pose différents problèmes sémantiques : certains mots sont polysémiques, d'autres peuvent être des synonymes et être malgré tout différenciés, d'autres encore sont liés sémantiquement sans que cela soit pris en compte et enfin, certains mots perdent leur sens s'ils sont extraits de leur groupe nominal. Pour pallier ces problèmes, certaines méthodes ne représentent plus les textes par des mots mais par des concepts extraits d'une ontologie de domaine, intégrant ainsi la notion de sens au modèle. Les modèles intégrant la représentation des textes par des concepts restent peu utilisés à cause des résultats peu satisfaisants. Afin d'améliorer les performances de ces modèles, plusieurs méthodes ont été proposées pour enrichir les caractéristiques des textes à l'aide de nouveaux concepts extraits de bases de connaissances. Mes travaux donnent suite à ces approches en proposant une étape d'enrichissement des modèles à l'aide d'une ontologie de domaine associée. J'ai proposé deux mesures permettant d'estimer l'appartenance aux catégories de ces nouveaux concepts. A l'aide de l'algorithme du classifieur naïf Bayésien, j'ai testé et comparé mes contributions sur le corpus de textes labéllisés Ohsumed et l'ontologie de domaine Disease Ontology. Les résultats satisfaisants m'ont amené à analyser plus précisément le rôle des relations sémantiques dans l'enrichissement des modèles. Ces nouveaux travaux ont été le sujet d'une seconde expérience où il est question d'évaluer les apports des relations hiérarchiques d'hyperonymie et d'hyponymie
Most of text-classification methods use the ``bag of words” paradigm to represent texts. However Bloahdom and Hortho have identified four limits to this representation: (1) some words are polysemics, (2) others can be synonyms and yet differentiated in the analysis, (3) some words are strongly semantically linked without being taken into account in the representation as such and (4) certain words lose their meaning if they are extracted from their nominal group. To overcome these problems, some methods no longer represent texts with words but with concepts extracted from a domain ontology (Bag of Concept), integrating the notion of meaning into the model. Models integrating the bag of concepts remain less used because of the unsatisfactory results, thus several methods have been proposed to enrich text features using new concepts extracted from knowledge bases. My work follows these approaches by proposing a model-enrichment step using a domain ontology, I proposed two measures to estimate to belong to the categories of these new concepts. Using the naive Bayes classifier algorithm, I tested and compared my contributions on the Ohsumed corpus using the domain ontology ``Disease Ontology”. The satisfactory results led me to analyse more precisely the role of semantic relations in the enrichment step. These new works have been the subject of a second experiment in which we evaluate the contributions of the hierarchical relations of hypernymy and hyponymy
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Hönninger, Jan. "Smart City concepts and their approach on sustainability, transportation and tourism – Waterborne transportation, an opportunity for sustainability?" Thesis, Umeå universitet, Institutionen för geografi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-182461.

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Due to urbanization and the population of cities producing up to 75% of emission, Smart City concepts, looking at sustainability and more efficiency within the city, with the help of IoT and ICT based technology, are seen as an opportunity to act future-oriented, today. Construction and transportation are seen as the main contributors on the way of change from energy consumption to energy production. Enhancing infrastructure to improve the quality of all sorts of public transportation is thus of utter importance to governance, interested in Smart City concepts. Looking at the literature, waterborne transportation has not received much scientific attention in the context of being implemented into Smart City initiatives. This systematic literature research draws logical conclusions from the researched literature. The research concludes with a research agenda for future research to deepen the knowledge in the explanatory field of waterborne transportation making use of Smart City technologies. The main findings of this thesis are: First, waterborne transportation poses a threat to the environment and impacts sustainability of water bodies, as well as the environment surrounding them. Second, Smart City technologies can successfully be implemented in waterborne transportation when carefully planned. Barriers for the implementation of Smart City concepts can be lack of knowledge, investment, data security and readiness of infrastructure. These can be overcome through the help of collaboration and knowledge sharing among the involved stakeholders. Third, the image of the industry can be shifted, as well as its direct impact and the indirect use of waterborne transportation can be made more sustainable and ecosystem friendly. This transition attracts further customers, who otherwise were not willing to use waterborne transportation. In order to make waterborne transportation more sustainable and part of the Smart City movement, knowledge needs to be deepened and awareness about the topic needs to be spread. Its use of Smart City technologies needs to be further investigated, looking at specific types and tailored solutions for them, as well as how beneficial such an investment can be for governments and companies regarding ecological costs and their image. This thesis mainly aims to help scholars, interested in further research to deepen the knowledge on waterborne transportation in a sustainability context, but also companies and governance, looking to make waterborne transportation more sustainable.
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Gutierres, Luna Neide Macedo. "O conceito de big data: novos desafios, novas oportunidades." Pontifícia Universidade Católica de São Paulo, 2017. https://tede2.pucsp.br/handle/handle/20455.

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Submitted by Filipe dos Santos (fsantos@pucsp.br) on 2017-10-03T12:32:00Z No. of bitstreams: 1 Luna Neide Macedo Gutierres.pdf: 2504303 bytes, checksum: 02a4e9360ce4e69a8c820a68f718d39a (MD5)
Made available in DSpace on 2017-10-03T12:32:00Z (GMT). No. of bitstreams: 1 Luna Neide Macedo Gutierres.pdf: 2504303 bytes, checksum: 02a4e9360ce4e69a8c820a68f718d39a (MD5) Previous issue date: 2017-09-19
The world faces exponential data growth. Data is created by smart devices, RFID technologies (Radio-Frequency IDentification), sensors, social networks, video surveillance and more. These generated data are no longer considered static, whose usefulness ends after the purpose of the collection is reached, they have become the raw material of the business, a vital economic resource, used to create a new form of economic value. Then comes the concept of “big data”. The objective of this research is to raise the discussion about the concept of big data, drawing from the current literature definitions that offer subsidies for the understanding of its real meaning and impact in the generation of useful ideas and goods and services of significant value. However, because it is a recent theme, the available literature is scarce. It is an applied purpose research with a descriptive purpose and uses the qualitative method of approach. It has by type of research the review of the literature for the theoretical basis, and also the study review of two cases through an exploratory research to collect the data to be analyzed. It seeks to confront the theory with the identified hypotheses and practices, to assess its adherence, arriving at informed conclusions, and to suggest future studies that may continue this line
O mundo enfrenta um crescimento exponencial de dados. Dados são criados por dispositivos inteligentes, tecnologias RFID (Radio-Frequency IDentification), sensores, redes sociais, vigilância por vídeo e muito mais. Esses dados gerados não são mais considerados estáticos, cuja utilidade termina depois que o objetivo da coleta é alcançado, eles se tornaram a matéria-prima dos negócios, um recurso econômico vital, usado para criar uma nova forma de valor econômico. Surge então o conceito de “big data”. O objetivo desta pesquisa é levantar a discussão sobre o conceito de big data, extraindo da literatura atual definições que ofereçam subsídios para o entendimento de seu real significado e impacto na geração de ideias úteis e bens e serviços de valor significativo. Entretanto, por ser um tema recente, a literatura disponível é escassa. É uma investigação de finalidade aplicada, com um objetivo descritivo e utiliza o método qualitativo de abordagem. Tem por tipo de pesquisa a revisão da literatura para a fundamentação teórica, e também a revisão de estudo de dois casos através de pesquisa exploratória para a coleta dos dados a serem analisados. Busca confrontar a teoria com as hipóteses e práticas identificadas, para avaliar sua aderência, chegando em conclusões fundamentadas, além de sugerir estudos futuros que podem dar continuidade a esta linha abordada
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Sonning, Sabina. "Big Data - Small Device: AMobile Design Concept fo rGeopolitical Awareness when Traveling." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-87203.

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This work explores an application concept for small mobile devices, displaying structured "Big Data" based on human web reporting. The target user is a traveler interested in geopolitical events in the visited region and the concept focuses on high level signals to describethe situation and allows for following up, down to original reporting sources. Interviews and a survey was used to investigate the target user group's current behavior and needs while traveling and in unstable regions. The design process is described in reference to interaction design practices and successful applications on the market today, resulting in aconcept presented in the form of high fidelity sketches, well documented interaction style and transitions, and a clickable low delity prototype. The work can be used as a reference document for further development.
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Montiel, López Jacob. "Fast and slow machine learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT014/document.

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L'ère du Big Data a révolutionné la manière dont les données sont créées et traitées. Dans ce contexte, de nombreux défis se posent, compte tenu de la quantité énorme de données disponibles qui doivent être efficacement gérées et traitées afin d’extraire des connaissances. Cette thèse explore la symbiose de l'apprentissage en mode batch et en flux, traditionnellement considérés dans la littérature comme antagonistes, sur le problème de la classification à partir de flux de données en évolution. L'apprentissage en mode batch est une approche bien établie basée sur une séquence finie: d'abord les données sont collectées, puis les modèles prédictifs sont créés, finalement le modèle est appliqué. Par contre, l’apprentissage par flux considère les données comme infinies, rendant le problème d’apprentissage comme une tâche continue (sans fin). De plus, les flux de données peuvent évoluer dans le temps, ce qui signifie que la relation entre les caractéristiques et la réponse correspondante peut changer. Nous proposons un cadre systématique pour prévoir le surendettement, un problème du monde réel ayant des implications importantes dans la société moderne. Les deux versions du mécanisme d'alerte précoce (batch et flux) surpassent les performances de base de la solution mise en œuvre par le Groupe BPCE, la deuxième institution bancaire en France. De plus, nous introduisons une méthode d'imputation évolutive basée sur un modèle pour les données manquantes dans la classification. Cette méthode présente le problème d'imputation sous la forme d'un ensemble de tâches de classification / régression résolues progressivement.Nous présentons un cadre unifié qui sert de plate-forme d'apprentissage commune où les méthodes de traitement par batch et par flux peuvent interagir de manière positive. Nous montrons que les méthodes batch peuvent être efficacement formées sur le réglage du flux dans des conditions spécifiques. Nous proposons également une adaptation de l'Extreme Gradient Boosting algorithme aux flux de données en évolution. La méthode adaptative proposée génère et met à jour l'ensemble de manière incrémentielle à l'aide de mini-lots de données. Enfin, nous présentons scikit-multiflow, un framework open source en Python qui comble le vide en Python pour une plate-forme de développement/recherche pour l'apprentissage à partir de flux de données en évolution
The Big Data era has revolutionized the way in which data is created and processed. In this context, multiple challenges arise given the massive amount of data that needs to be efficiently handled and processed in order to extract knowledge. This thesis explores the symbiosis of batch and stream learning, which are traditionally considered in the literature as antagonists. We focus on the problem of classification from evolving data streams.Batch learning is a well-established approach in machine learning based on a finite sequence: first data is collected, then predictive models are created, then the model is applied. On the other hand, stream learning considers data as infinite, rendering the learning problem as a continuous (never-ending) task. Furthermore, data streams can evolve over time, meaning that the relationship between features and the corresponding response (class in classification) can change.We propose a systematic framework to predict over-indebtedness, a real-world problem with significant implications in modern society. The two versions of the early warning mechanism (batch and stream) outperform the baseline performance of the solution implemented by the Groupe BPCE, the second largest banking institution in France. Additionally, we introduce a scalable model-based imputation method for missing data in classification. This method casts the imputation problem as a set of classification/regression tasks which are solved incrementally.We present a unified framework that serves as a common learning platform where batch and stream methods can positively interact. We show that batch methods can be efficiently trained on the stream setting under specific conditions. The proposed hybrid solution works under the positive interactions between batch and stream methods. We also propose an adaptation of the Extreme Gradient Boosting (XGBoost) algorithm for evolving data streams. The proposed adaptive method generates and updates the ensemble incrementally using mini-batches of data. Finally, we introduce scikit-multiflow, an open source framework in Python that fills the gap in Python for a development/research platform for learning from evolving data streams
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Nybacka, A. (Aino). "Privacy concerns of consumers in big data management for marketing purposes:an integrative literature review." Bachelor's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201605261989.

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This bachelor’s thesis is a literature review about big data, privacy concerns for individual consumers and how these two overlap together in a way to possibly explain what are the privacy concerns that customers have, and the companies maybe should think about, when they utilize data as a marketing tool. The thesis introduces an integrative framework for the privacy concerns emerging from the process of big data management for marketing purposes and gives insights for phases in this process first separately from companies point of view, then from the consumers point of view regarding the privacy issues and then together by bringing these two issues together.
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Rantzau, Ralf. "Extended concepts for association rule discovery." [S.l. : s.n.], 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937694.

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Malik, Zeeshan. "Towards on-line domain-independent big data learning : novel theories and applications." Thesis, University of Stirling, 2015. http://hdl.handle.net/1893/22591.

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Feature extraction is an extremely important pre-processing step to pattern recognition, and machine learning problems. This thesis highlights how one can best extract features from the data in an exhaustively online and purely adaptive manner. The solution to this problem is given for both labeled and unlabeled datasets, by presenting a number of novel on-line learning approaches. Specifically, the differential equation method for solving the generalized eigenvalue problem is used to derive a number of novel machine learning and feature extraction algorithms. The incremental eigen-solution method is used to derive a novel incremental extension of linear discriminant analysis (LDA). Further the proposed incremental version is combined with extreme learning machine (ELM) in which the ELM is used as a preprocessor before learning. In this first key contribution, the dynamic random expansion characteristic of ELM is combined with the proposed incremental LDA technique, and shown to offer a significant improvement in maximizing the discrimination between points in two different classes, while minimizing the distance within each class, in comparison with other standard state-of-the-art incremental and batch techniques. In the second contribution, the differential equation method for solving the generalized eigenvalue problem is used to derive a novel state-of-the-art purely incremental version of slow feature analysis (SLA) algorithm, termed the generalized eigenvalue based slow feature analysis (GENEIGSFA) technique. Further the time series expansion of echo state network (ESN) and radial basis functions (EBF) are used as a pre-processor before learning. In addition, the higher order derivatives are used as a smoothing constraint in the output signal. Finally, an online extension of the generalized eigenvalue problem, derived from James Stone’s criterion, is tested, evaluated and compared with the standard batch version of the slow feature analysis technique, to demonstrate its comparative effectiveness. In the third contribution, light-weight extensions of the statistical technique known as canonical correlation analysis (CCA) for both twinned and multiple data streams, are derived by using the same existing method of solving the generalized eigenvalue problem. Further the proposed method is enhanced by maximizing the covariance between data streams while simultaneously maximizing the rate of change of variances within each data stream. A recurrent set of connections used by ESN are used as a pre-processor between the inputs and the canonical projections in order to capture shared temporal information in two or more data streams. A solution to the problem of identifying a low dimensional manifold on a high dimensional dataspace is then presented in an incremental and adaptive manner. Finally, an online locally optimized extension of Laplacian Eigenmaps is derived termed the generalized incremental laplacian eigenmaps technique (GENILE). Apart from exploiting the benefit of the incremental nature of the proposed manifold based dimensionality reduction technique, most of the time the projections produced by this method are shown to produce a better classification accuracy in comparison with standard batch versions of these techniques - on both artificial and real datasets.
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Books on the topic "Big data concepts"

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Yu, Shui, and Song Guo, eds. Big Data Concepts, Theories, and Applications. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27763-9.

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Cutt, Shannon, ed. Practical Statistics for Data Scientists: 50 Essential Concepts. Beijing: O’Reilly Media, 2017.

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Wender, Ben A., ed. Refining the Concept of Scientific Inference When Working with Big Data. Washington, D.C.: National Academies Press, 2017. http://dx.doi.org/10.17226/24654.

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Yu, Shui, and Song Guo. Big Data Concepts, Theories, and Applications. Springer, 2018.

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Gandomi, Amir H., Balamurugan Balusamy, and Nandhini Abirami R. Big Data: Concepts, Technology, and Architecture. Wiley & Sons, Limited, John, 2021.

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Costa, Carlos, and Maribel Yasmina Santos. Big Data: Concepts, Warehousing, and Analytics. River Publishers, 2020.

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Yu, Shui, and Song Guo. Big Data Concepts, Theories, and Applications. Springer, 2016.

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Costa, Carlos, and Maribel Yasmina Santos. Big Data: Concepts, Warehousing, and Analytics. River Publishers, 2020.

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Jo, Taeho. Text Mining: Concepts, Implementation, and Big Data Challenge (Studies in Big Data). Springer, 2018.

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Association, Information Resources Management. Big Data: Concepts, Methodologies, Tools, and Applications. IGI Global, 2016.

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Book chapters on the topic "Big data concepts"

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Holeňa, Martin, Petr Pulc, and Martin Kopp. "Basic Concepts Concerning Classification." In Studies in Big Data, 69–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36962-0_2.

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Fang, Bin, and Peng Zhang. "Big Data in Finance." In Big Data Concepts, Theories, and Applications, 391–412. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27763-9_11.

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Wen, Mi, Shui Yu, Jinguo Li, Hongwei Li, and Kejie Lu. "Big Data Storage Security." In Big Data Concepts, Theories, and Applications, 237–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27763-9_6.

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Rutkowski, Leszek, Maciej Jaworski, and Piotr Duda. "Basic Concepts of Data Stream Mining." In Studies in Big Data, 13–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13962-9_2.

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Rutkowski, Leszek, Maciej Jaworski, and Piotr Duda. "Basic Concepts of Probabilistic Neural Networks." In Studies in Big Data, 117–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13962-9_8.

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El-Din, Doaa Mohey, Aboul Ella Hassanein, and Ehab E. Hassanien. "Smart Environments Concepts, Applications, and Challenges." In Studies in Big Data, 493–519. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59338-4_24.

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Mazumder, Sourav. "Big Data Tools and Platforms." In Big Data Concepts, Theories, and Applications, 29–128. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27763-9_2.

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Saxena, Ankur, Shivani Singh, and Chetna Shakya. "Concepts of HBase Archetypes in Big Data Engineering." In Studies in Big Data, 83–111. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8476-8_5.

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Chebbi, Imen, Wadii Boulila, and Imed Riadh Farah. "Big Data: Concepts, Challenges and Applications." In Computational Collective Intelligence, 638–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24306-1_62.

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Ishikawa, Hiroshi, and Yukio Yamamoto. "Social Big Data: Concepts and Theory." In Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVII, 51–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62919-2_3.

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Conference papers on the topic "Big data concepts"

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Gates, Mark, Hartwig Anzt, Jakub Kurzak, and Jack Dongarra. "Accelerating collaborative filtering using concepts from high performance computing." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363811.

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Kim, Youngho, Petros Zerfos, Vadim Sheinin, and Nancy Greco. "Ranking the importance of ontology concepts using document summarization techniques." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258079.

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Bertino, Elisa. "Data privacy for IoT systems: Concepts, approaches, and research directions." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7841030.

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Keloth, Vipina K., Shuxin Zhou, Luke Lindemann, Gai Elhanan, Andrew J. Einstein, James Geller, and Yehoshua Perl. "Mining Concepts for a COVID Interface Terminology for Annotation of EHRs." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377981.

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Bhatnagar, Raj, and Lalit Kumar. "An efficient map-reduce algorithm for computing formal concepts from binary data." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363915.

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Bertino, Elisa, Geeth de Mel, Alessandra Russo, Seraphin Calo, and Dinesh Verma. "Community-based self generation of policies and processes for assets: Concepts and research directions." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258265.

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Elarabi, Tarek, Bhanu Sharma, Karan Pahwa, and Vishal Deep. "Big data analytics concepts and management techniques." In 2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2016. http://dx.doi.org/10.1109/inventive.2016.7824813.

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Armour, Frank, Stephen Kaisler, and Alberto Espinosa. "Introduction to Big Data Analytics: Concepts, Methods, Techniques Minitrack." In 2015 48th Hawaii International Conference on System Sciences (HICSS). IEEE, 2015. http://dx.doi.org/10.1109/hicss.2015.650.

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Joseph, Daniel, Nikolay Mehandjiev, Babis Theodoulidis, John Davies, and Ian Thurlow. "Identifying Relevant Formal Concepts through the Collapse Index." In 2015 IEEE International Congress on Big Data (BigData Congress). IEEE, 2015. http://dx.doi.org/10.1109/bigdatacongress.2015.37.

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Alguliyev, Rasim M., Ramiz M. Aliguliyev, and Makrufa S. Hajirahimova. "Big data integration architectural concepts for oil and gas industry." In 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2016. http://dx.doi.org/10.1109/icaict.2016.7991832.

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Reports on the topic "Big data concepts"

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Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, A. Takbiri Borujeni, and E. Fathi. Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part One: Proof of Concept; NETL-PUB-21574; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2017. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1417305.

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Hunter, Fraser, and Martin Carruthers. Iron Age Scotland. Society for Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.193.

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The main recommendations of the panel report can be summarised under five key headings:  Building blocks: The ultimate aim should be to build rich, detailed and testable narratives situated within a European context, and addressing phenomena from the longue durée to the short-term over international to local scales. Chronological control is essential to this and effective dating strategies are required to enable generation-level analysis. The ‘serendipity factor’ of archaeological work must be enhanced by recognising and getting the most out of information-rich sites as they appear. o There is a pressing need to revisit the archives of excavated sites to extract more information from existing resources, notably through dating programmes targeted at regional sequences – the Western Isles Atlantic roundhouse sequence is an obvious target. o Many areas still lack anything beyond the baldest of settlement sequences, with little understanding of the relations between key site types. There is a need to get at least basic sequences from many more areas, either from sustained regional programmes or targeted sampling exercises. o Much of the methodologically innovative work and new insights have come from long-running research excavations. Such large-scale research projects are an important element in developing new approaches to the Iron Age.  Daily life and practice: There remains great potential to improve the understanding of people’s lives in the Iron Age through fresh approaches to, and integration of, existing and newly-excavated data. o House use. Rigorous analysis and innovative approaches, including experimental archaeology, should be employed to get the most out of the understanding of daily life through the strengths of the Scottish record, such as deposits within buildings, organic preservation and waterlogging. o Material culture. Artefact studies have the potential to be far more integral to understandings of Iron Age societies, both from the rich assemblages of the Atlantic area and less-rich lowland finds. Key areas of concern are basic studies of material groups (including the function of everyday items such as stone and bone tools, and the nature of craft processes – iron, copper alloy, bone/antler and shale offer particularly good evidence). Other key topics are: the role of ‘art’ and other forms of decoration and comparative approaches to assemblages to obtain synthetic views of the uses of material culture. o Field to feast. Subsistence practices are a core area of research essential to understanding past society, but different strands of evidence need to be more fully integrated, with a ‘field to feast’ approach, from production to consumption. The working of agricultural systems is poorly understood, from agricultural processes to cooking practices and cuisine: integrated work between different specialisms would assist greatly. There is a need for conceptual as well as practical perspectives – e.g. how were wild resources conceived? o Ritual practice. There has been valuable work in identifying depositional practices, such as deposition of animals or querns, which are thought to relate to house-based ritual practices, but there is great potential for further pattern-spotting, synthesis and interpretation. Iron Age Scotland: ScARF Panel Report v  Landscapes and regions:  Concepts of ‘region’ or ‘province’, and how they changed over time, need to be critically explored, because they are contentious, poorly defined and highly variable. What did Iron Age people see as their geographical horizons, and how did this change?  Attempts to understand the Iron Age landscape require improved, integrated survey methodologies, as existing approaches are inevitably partial.  Aspects of the landscape’s physical form and cover should be investigated more fully, in terms of vegetation (known only in outline over most of the country) and sea level change in key areas such as the firths of Moray and Forth.  Landscapes beyond settlement merit further work, e.g. the use of the landscape for deposition of objects or people, and what this tells us of contemporary perceptions and beliefs.  Concepts of inherited landscapes (how Iron Age communities saw and used this longlived land) and socal resilience to issues such as climate change should be explored more fully.  Reconstructing Iron Age societies. The changing structure of society over space and time in this period remains poorly understood. Researchers should interrogate the data for better and more explicitly-expressed understandings of social structures and relations between people.  The wider context: Researchers need to engage with the big questions of change on a European level (and beyond). Relationships with neighbouring areas (e.g. England, Ireland) and analogies from other areas (e.g. Scandinavia and the Low Countries) can help inform Scottish studies. Key big topics are: o The nature and effect of the introduction of iron. o The social processes lying behind evidence for movement and contact. o Parallels and differences in social processes and developments. o The changing nature of houses and households over this period, including the role of ‘substantial houses’, from crannogs to brochs, the development and role of complex architecture, and the shift away from roundhouses. o The chronology, nature and meaning of hillforts and other enclosed settlements. o Relationships with the Roman world
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African Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.

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This report maps the African landscape of Open Science – with a focus on Open Data as a sub-set of Open Science. Data to inform the landscape study were collected through a variety of methods, including surveys, desk research, engagement with a community of practice, networking with stakeholders, participation in conferences, case study presentations, and workshops hosted. Although the majority of African countries (35 of 54) demonstrates commitment to science through its investment in research and development (R&D), academies of science, ministries of science and technology, policies, recognition of research, and participation in the Science Granting Councils Initiative (SGCI), the following countries demonstrate the highest commitment and political willingness to invest in science: Botswana, Ethiopia, Kenya, Senegal, South Africa, Tanzania, and Uganda. In addition to existing policies in Science, Technology and Innovation (STI), the following countries have made progress towards Open Data policies: Botswana, Kenya, Madagascar, Mauritius, South Africa and Uganda. Only two African countries (Kenya and South Africa) at this stage contribute 0.8% of its GDP (Gross Domestic Product) to R&D (Research and Development), which is the closest to the AU’s (African Union’s) suggested 1%. Countries such as Lesotho and Madagascar ranked as 0%, while the R&D expenditure for 24 African countries is unknown. In addition to this, science globally has become fully dependent on stable ICT (Information and Communication Technologies) infrastructure, which includes connectivity/bandwidth, high performance computing facilities and data services. This is especially applicable since countries globally are finding themselves in the midst of the 4th Industrial Revolution (4IR), which is not only “about” data, but which “is” data. According to an article1 by Alan Marcus (2015) (Senior Director, Head of Information Technology and Telecommunications Industries, World Economic Forum), “At its core, data represents a post-industrial opportunity. Its uses have unprecedented complexity, velocity and global reach. As digital communications become ubiquitous, data will rule in a world where nearly everyone and everything is connected in real time. That will require a highly reliable, secure and available infrastructure at its core, and innovation at the edge.” Every industry is affected as part of this revolution – also science. An important component of the digital transformation is “trust” – people must be able to trust that governments and all other industries (including the science sector), adequately handle and protect their data. This requires accountability on a global level, and digital industries must embrace the change and go for a higher standard of protection. “This will reassure consumers and citizens, benefitting the whole digital economy”, says Marcus. A stable and secure information and communication technologies (ICT) infrastructure – currently provided by the National Research and Education Networks (NRENs) – is key to advance collaboration in science. The AfricaConnect2 project (AfricaConnect (2012–2014) and AfricaConnect2 (2016–2018)) through establishing connectivity between National Research and Education Networks (NRENs), is planning to roll out AfricaConnect3 by the end of 2019. The concern however is that selected African governments (with the exception of a few countries such as South Africa, Mozambique, Ethiopia and others) have low awareness of the impact the Internet has today on all societal levels, how much ICT (and the 4th Industrial Revolution) have affected research, and the added value an NREN can bring to higher education and research in addressing the respective needs, which is far more complex than simply providing connectivity. Apart from more commitment and investment in R&D, African governments – to become and remain part of the 4th Industrial Revolution – have no option other than to acknowledge and commit to the role NRENs play in advancing science towards addressing the SDG (Sustainable Development Goals). For successful collaboration and direction, it is fundamental that policies within one country are aligned with one another. Alignment on continental level is crucial for the future Pan-African African Open Science Platform to be successful. Both the HIPSSA ((Harmonization of ICT Policies in Sub-Saharan Africa)3 project and WATRA (the West Africa Telecommunications Regulators Assembly)4, have made progress towards the regulation of the telecom sector, and in particular of bottlenecks which curb the development of competition among ISPs. A study under HIPSSA identified potential bottlenecks in access at an affordable price to the international capacity of submarine cables and suggested means and tools used by regulators to remedy them. Work on the recommended measures and making them operational continues in collaboration with WATRA. In addition to sufficient bandwidth and connectivity, high-performance computing facilities and services in support of data sharing are also required. The South African National Integrated Cyberinfrastructure System5 (NICIS) has made great progress in planning and setting up a cyberinfrastructure ecosystem in support of collaborative science and data sharing. The regional Southern African Development Community6 (SADC) Cyber-infrastructure Framework provides a valuable roadmap towards high-speed Internet, developing human capacity and skills in ICT technologies, high- performance computing and more. The following countries have been identified as having high-performance computing facilities, some as a result of the Square Kilometre Array7 (SKA) partnership: Botswana, Ghana, Kenya, Madagascar, Mozambique, Mauritius, Namibia, South Africa, Tunisia, and Zambia. More and more NRENs – especially the Level 6 NRENs 8 (Algeria, Egypt, Kenya, South Africa, and recently Zambia) – are exploring offering additional services; also in support of data sharing and transfer. The following NRENs already allow for running data-intensive applications and sharing of high-end computing assets, bio-modelling and computation on high-performance/ supercomputers: KENET (Kenya), TENET (South Africa), RENU (Uganda), ZAMREN (Zambia), EUN (Egypt) and ARN (Algeria). Fifteen higher education training institutions from eight African countries (Botswana, Benin, Kenya, Nigeria, Rwanda, South Africa, Sudan, and Tanzania) have been identified as offering formal courses on data science. In addition to formal degrees, a number of international short courses have been developed and free international online courses are also available as an option to build capacity and integrate as part of curricula. The small number of higher education or research intensive institutions offering data science is however insufficient, and there is a desperate need for more training in data science. The CODATA-RDA Schools of Research Data Science aim at addressing the continental need for foundational data skills across all disciplines, along with training conducted by The Carpentries 9 programme (specifically Data Carpentry 10 ). Thus far, CODATA-RDA schools in collaboration with AOSP, integrating content from Data Carpentry, were presented in Rwanda (in 2018), and during17-29 June 2019, in Ethiopia. Awareness regarding Open Science (including Open Data) is evident through the 12 Open Science-related Open Access/Open Data/Open Science declarations and agreements endorsed or signed by African governments; 200 Open Access journals from Africa registered on the Directory of Open Access Journals (DOAJ); 174 Open Access institutional research repositories registered on openDOAR (Directory of Open Access Repositories); 33 Open Access/Open Science policies registered on ROARMAP (Registry of Open Access Repository Mandates and Policies); 24 data repositories registered with the Registry of Data Repositories (re3data.org) (although the pilot project identified 66 research data repositories); and one data repository assigned the CoreTrustSeal. Although this is a start, far more needs to be done to align African data curation and research practices with global standards. Funding to conduct research remains a challenge. African researchers mostly fund their own research, and there are little incentives for them to make their research and accompanying data sets openly accessible. Funding and peer recognition, along with an enabling research environment conducive for research, are regarded as major incentives. The landscape report concludes with a number of concerns towards sharing research data openly, as well as challenges in terms of Open Data policy, ICT infrastructure supportive of data sharing, capacity building, lack of skills, and the need for incentives. Although great progress has been made in terms of Open Science and Open Data practices, more awareness needs to be created and further advocacy efforts are required for buy-in from African governments. A federated African Open Science Platform (AOSP) will not only encourage more collaboration among researchers in addressing the SDGs, but it will also benefit the many stakeholders identified as part of the pilot phase. The time is now, for governments in Africa, to acknowledge the important role of science in general, but specifically Open Science and Open Data, through developing and aligning the relevant policies, investing in an ICT infrastructure conducive for data sharing through committing funding to making NRENs financially sustainable, incentivising open research practices by scientists, and creating opportunities for more scientists and stakeholders across all disciplines to be trained in data management.
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