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Littérature scientifique sur le sujet « Cloud-based big data analytics »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Cloud-based big data analytics"

Msweli, Andile Precious, Tshinakaho Seaba, Victor Ntala Paledi, and KHULISO SIGAMA. "Technology Factors Required for Adopting Cloud-Based Big Data Analytics in South African Banking." International Journal of Science Annals 7, no. 2 (2025): 47–55. https://doi.org/10.26697/ijsa.2024.2.5.

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Background and Aim of Study: South African banks are generally known for early technology adoption. While this is so, there is a need to integrate some of the fourth industrial revolution technologies such as big data analytics and cloud computing collectively referred to as cloud-based big data analytics; and subsequently consider technology related aspects required for adopting integrated technologies of this nature.The aim of the study is to identify technology related factors that are necessary for adopting cloud-based big data analytics in South African banking.<stro

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Sabbani, Goutham. "Big Data Analytics in Cloud Computing." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 359–63. http://dx.doi.org/10.21275/sr24604002336.

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C, Pradeep, and Prof Rahul Pawar. "Big Data Analytics in Cloud Environments." International Journal of Research Publication and Reviews 5, no. 3 (2024): 4240–46. http://dx.doi.org/10.55248/gengpi.5.0324.07105.

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Researcher. "Cloud-Based AI and Big Data Analytics for Real-Time Business Decision-Making." International Journal of Finance (IJFIN) 36, no. 6 (2023): 96–123. https://doi.org/10.5281/zenodo.14905134.

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The rising sun of technological development has arrived to illuminate and innovate the traditional business operational processes. Providing academic and practical contributions, this essay explores the effect of cloud-based artificial intelligence and big data analytics on business decision-making. It is observed that cloud-based AI and big data analytics support real-time business decision-making activities. Unlike the traditional business decision support framework, contemporary business decision-support systems depend on different categories of data analysis fields such as artificial i

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Vistro, Daniel Mago. "IoT based Big Data Analytics for Cloud Storage Using Edge Computing." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1594–98. http://dx.doi.org/10.5373/jardcs/v12sp7/20202262.

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Anugraha, P. P. Hiba Fathima K. P. "Big Data Analytics In Cloud Computing." International Journal of Scientific Research and Technology 2, no. 1 (2025): 167–75. https://doi.org/10.5281/zenodo.14637762.

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The convergence of big data and cloud comput- ing offers numerous advantages, including scalability, cost- effectiveness, flexibility, collaboration, and accessibility. Cloud platforms allow for seamless resource scaling, eliminating the need for heavy infrastructure investments. Paying only for utilized resources reduces upfront expenses. Cloud-based solu- tions provide flexibility in storage and processing capabilities, allowing for tailored adjustments as organizational needs evolve. Collaboration is fostered, enabling data sharing and teamwork among diverse users and teams. Accessibility b

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Liu, Xiang Ju. "Research of Big Data Processing Platform." Applied Mechanics and Materials 484-485 (January 2014): 922–26. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.922.

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This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture , big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play

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Umbu Zogara, Lukas, and Cecilia Dai Payon Binti Gabriel. "BIG DATA ANALYTICS FOR HEALTHCARE APPLICATIONS MOBILE CLOUD BASED." Scientific Journal of Information System 1, no. 1 (2024): 16–21. http://dx.doi.org/10.70429/sjis.v1i1.85.

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Mobile devices are increasingly becoming one and more indispensable part of our daily lives, as it facilitates to perform various useful tasks. Mobile cloud integrates mobile and cloud computing to extend the benefits of the cloud itself, and overcome limitations in times of cloud such as limited memory, CPU power, big data analytics technology allows extracting value from data that has four Vs: volume, variety, speed, and honesty. This paper discusses mobile cloud-based healthcare and big data analytics in its application. The conclusion is drawn about the design of healthcare systems using b

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Yilmaz, Nesim, Tuncer Demir, Safak Kaplan, and Sevilin Demirci. "Demystifying Big Data Analytics in Cloud Computing." Fusion of Multidisciplinary Research, An International Journal 1, no. 01 (2020): 25–36. https://doi.org/10.63995/dopv8398.

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Big Data Analytics in cloud computing represents a transformative synergy, enabling the processing and analysis of vast datasets with unprecedented efficiency and scalability. The cloud provides a flexible and cost-effective infrastructure for storing, managing, and analyzing big data, addressing the limitations of traditional on-premises systems. This combination allows organizations to harness the full potential of big data, deriving actionable insights to drive decision-making and innovation. The integration of big data analytics with cloud computing leverages advanced technologies such as

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Wang, Ruoyu, Daniel Sun, Guoqiang Li, Raymond Wong, and Shiping Chen. "Pipeline provenance for cloud‐based big data analytics." Software: Practice and Experience 50, no. 5 (2020): 658–74. http://dx.doi.org/10.1002/spe.2744.

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Thèses sur le sujet "Cloud-based big data analytics"

Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.

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This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field. Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand. The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies

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Saker, Vanessa. "Automated feature synthesis on big data using cloud computing resources." Master's thesis, University of Cape Town, 2020. http://hdl.handle.net/11427/32452.

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The data analytics process has many time-consuming steps. Combining data that sits in a relational database warehouse into a single relation while aggregating important information in a meaningful way and preserving relationships across relations, is complex and time-consuming. This step is exceptionally important as many machine learning algorithms require a single file format as an input (e.g. supervised and unsupervised learning, feature representation and feature learning, etc.). An analyst is required to manually combine relations while generating new, more impactful information points fr

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Flatt, Taylor. "CrowdCloud: Combining Crowdsourcing with Cloud Computing for SLO Driven Big Data Analysis." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2234.

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The evolution of structured data from simple rows and columns on a spreadsheet to more complex unstructured data such as tweets, videos, voice, and others, has resulted in a need for more adaptive analytical platforms. It is estimated that upwards of 80% of data on the Internet today is unstructured. There is a drastic need for crowdsourcing platforms to perform better in the wake of the tsunami of data. We investigated the employment of a monitoring service which would allow the system take corrective action in the event the results were trending in away from meeting the accuracy, budget, and

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Rashid, A. N. M. Bazlur. "Cooperative co-evolution-based feature selection for big data analytics." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2428.

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The rapid progress of modern technologies generates a massive amount of highthroughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (attributes). However, irrelevant features may degrade the classification performance of ML algorithms. Feature selection (FS) is a combinatorial optimisation technique used to select a subset of relevant features that represent the dataset. For example, FS is an effective preprocessing step of anomaly detection techniques in Big Cybersecurity Datasets. Evolutiona

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Sellén, David. "Big Data analytics for the forest industry : A proof-of-conceptbuilt on cloud technologies." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-28541.

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Large amounts of data in various forms are generated at a fast pace in today´s society. This is commonly referred to as “Big Data”. Making use of Big Data has been increasingly important for both business and in research. The forest industry is generating big amounts of data during the different processes of forest harvesting. In Sweden, forest infor-mation is sent to SDC, the information hub for the Swedish forest industry. In 2014, SDC received reports on 75.5 million m3fub from harvester and forwarder machines. These machines use a global stand-ard called StanForD 2010 for communication and

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Olsén, Cleas, and Gustav Lindskog. "Big Data Analytics : A potential way to Competitive Performance." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104372.

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Big data analytics (BDA) has become an increasingly popular topic over the years amongst academics and practitioners alike. Big data, which is an important part of BDA, was originally defined with three Vs, being volume, velocity and variety. In later years more Vs have surfaced to better accommodate the current need. The analytics of BDA consists of different methods of analysing gathered data. Analysing data can provide insights to organisations which in turn can give organisations competitive advantage and enhance their businesses. Looking into the necessary resources needed to build big da

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Denadija, Feda, and David Löfgren. "Revealing the Non-technical Side of Big Data Analytics : Evidence from Born analyticals and Big intelligent firms." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-298137.

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This study aspired to gain a more a nuanced understanding of the emerging analytics technologies and the vital capabilities that ultimately drive evidence-based decision making. Big data technology is widely discussed by varying groups in society and believed to revolutionize corporate decision making. In spite of big data's promising possibilities only a trivial fraction of firms deploying big data analytics (BDA) have gained significant benefits from their initiatives. Trying to explain this inability we leaned back on prior IT literature suggesting that IT resources can only be successfully

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Barros, Victor Perazzolo. "Big data analytics em cloud gaming: um estudo sobre o reconhecimento de padrões de jogadores." Universidade Presbiteriana Mackenzie, 2017. http://tede.mackenzie.br/jspui/handle/tede/3405.

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Submitted by Rosa Assis (rosa_assis@yahoo.com.br) on 2017-11-14T18:05:03Z No. of bitstreams: 2 VICTOR PERAZZOLO BARROS.pdf: 24134660 bytes, checksum: 8761000aa9ba093f81a14b3c2368f2b7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Approved for entry into archive by Paola Damato (repositorio@mackenzie.br) on 2017-11-27T12:14:38Z (GMT) No. of bitstreams: 2 VICTOR PERAZZOLO BARROS.pdf: 24134660 bytes, checksum: 8761000aa9ba093f81a14b3c2368f2b7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Made available in DSpace on 2017-11-27T12:1

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Ceccarello, Matteo. "Clustering-based Algorithms for Big Data Computations." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3424849.

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In the age of big data, the amount of information that applications need to process often exceeds the computational capabilities of single machines. To cope with this deluge of data, new computational models have been defined. The MapReduce model allows the development of distributed algorithms targeted at large clusters, where each machine can only store a small fraction of the data. In the streaming model a single processor processes on-the-fly an incoming stream of data, using only limited memory. The specific characteristics of these models combined with the necessity of processing ver

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Huai, Yin. "Building High Performance Data Analytics Systems based on Scale-out Models." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1427553721.

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Livres sur le sujet "Cloud-based big data analytics"

Trovati, Marcello, Richard Hill, Ashiq Anjum, Shao Ying Zhu, and Lu Liu, eds. Big-Data Analytics and Cloud Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25313-8.

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Das, Himansu, Rabindra K. Barik, Harishchandra Dubey, and Diptendu Sinha Roy, eds. Cloud Computing for Geospatial Big Data Analytics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03359-0.

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Sarma, Hiren Kumar Deva, Valentina Emilia Balas, Bhaskar Bhuyan, and Nitul Dutta, eds. Contemporary Issues in Communication, Cloud and Big Data Analytics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4244-9.

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Hosseinian-Far, Amin, Muthu Ramachandran, and Dilshad Sarwar, eds. Strategic Engineering for Cloud Computing and Big Data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52491-7.

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Upadhyay, Nitin. CABology: Value of Cloud, Analytics and Big Data Trio Wave. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8675-5.

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Kumar Rana, Arun, Sudeshna Chakraborty, Pallavi Goel, Sumit Kumar Rana, and Ahmed A. Elngar. Internet of Things and Big Data Analytics-Based Manufacturing. CRC Press, 2024. http://dx.doi.org/10.1201/9781032673479.

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Das, Sanjoy, Ram Shringar Rao, Indrani Das, Vishal Jain, and Nanhay Singh. Cloud Computing Enabled Big-Data Analytics in Wireless Ad-hoc Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003206453.

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Pani, Subhendu Kumar, Somanath Tripathy, Talal Ashraf Butt, Sumit Kundu, and George Jandieri. Applications of Machine Learning in Big-Data Analytics and Cloud Computing. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337218.

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Lotfi, Chaimaa, Swetha Srinivasan, Myriam Ertz, and Imen Latrous. Exploring the Aggregated and Granular Impact of Big Data Analytics on a Firm’s Performance Through Web Scraping-Based Methodology. SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529667394.

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FGBOU, VO. Digital analytics and financial security control of socially significant organizations. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1863937.

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The monograph is devoted to the formation of the concept of digital financial security analytics. The use of the digital environment and big data analysis tools in the system of monitoring sectoral risks and monitoring the activities of socially significant organizations from the position of the ESG strategy is disclosed. At the same time, financial security is considered as an aggregated result of the action of economic, environmental and social factors in a rapidly changing economy. It covers several key areas that make it possible to digitalize and improve the effectiveness of monitor

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Chapitres de livres sur le sujet "Cloud-based big data analytics"

Matter, Ulrich. "Cloud Computing." In Big Data Analytics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003378822-7.

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Oppitz, Marcus, and Peter Tomsu. "Big Data Analytics." In Inventing the Cloud Century. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61161-7_18.

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Demirbaga, Ümit, Gagangeet Singh Aujla, Anish Jindal, and Oğuzhan Kalyon. "Cloud Computing for Big Data Analytics." In Big Data Analytics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55639-5_4.

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Casturi, Rao, and Rajshekhar Sunderraman. "Distributed Financial Calculation Framework on Cloud Computing Environment." In Big Data Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04780-1_5.

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Wang, Lihui, and Xi Vincent Wang. "Big Data Analytics for Scheduling and Machining." In Cloud-Based Cyber-Physical Systems in Manufacturing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67693-7_14.

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Sanjeev, B. S., and Dheeraj Chitara. "Big Data over Cloud: Enabling Drug Design Under Cellular Environment." In Big Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_20.

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Gupta, Rajeev, Himanshu Gupta, and Mukesh Mohania. "Cloud Computing and Big Data Analytics: What Is New from Databases Perspective?" In Big Data Analytics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35542-4_5.

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Nascimento, Dimas C., Carlos Eduardo Pires, and Demetrio Mestre. "Data Quality Monitoring of Cloud Databases Based on Data Quality SLAs." In Big-Data Analytics and Cloud Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25313-8_1.

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Jugulum, Rajesh, David J. Fogarty, Chris Heien, and Surya Putchala. "Big Data and Cloud Solutions." In Big Data Management and Analytics. CRC Press, 2025. https://doi.org/10.1201/9781003190325-6.

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Mourya, Ashish Kumar, Shafqat-Ul-Ahsaan, and Sheikh Mohammad Idrees. "Cloud Computing-Based Approach for Accessing Electronic Health Record for Healthcare Sector." In Microservices in Big Data Analytics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0128-9_16.

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Actes de conférences sur le sujet "Cloud-based big data analytics"

Gupta, Shubham, Munikrishnaiah Sundararamaiah, and Geeta Geeta. "Leveraging Cloud-Native Data Engineering for Big Data Analytics." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011292.

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Zhou, Kun, Xinde Yi, Yaxiong Zhang, and Zhiqiang Wu. "A DDA-Based Cloud Computing System." In 2025 10th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 2025. https://doi.org/10.1109/icccbda64898.2025.11030504.

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Ordonez, Carlos, and Wojciech Macyna. "Optimizing Energy Consumed by Analytics in the Cloud." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825559.

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Tiwari, Trishita, Ata Turk, Alina Oprea, Katzalin Olcoz, and Ayse K. Coskun. "User-profile-based analytics for detecting cloud security breaches." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258494.

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Manekar, Amit Kumar, and G. Pradeepini. "Cloud Based Big Data Analytics a Review." In 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015. http://dx.doi.org/10.1109/cicn.2015.160.

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Mantzoukas, Konstantinos, Christos Kloukinas, and George Spanoudakis. "Monitoring Data Integrity in Big Data Analytics Services." In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). IEEE, 2018. http://dx.doi.org/10.1109/cloud.2018.00132.

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Babuji, Yadu N., Kyle Chard, Aaron Gerow, and Eamon Duede. "Cloud Kotta: Enabling secure and scalable data analytics in the cloud." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840616.

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Wu, Dazhong, Connor Jennings, Janis Terpenny, and Soundar Kumara. "Cloud-based machine learning for predictive analytics: Tool wear prediction in milling." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840831.

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Gehrmann, Christian, and Martin Gunnarsson. "An Identity Privacy Preserving IoT Data Protection Scheme for Cloud Based Analytics." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006017.

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Alrehaili, Ghadeer, Najla Galam, Rawan Alawad, and Lamya Albraheem. "Cloud-Based Big Data Analytics on IoT Applications." In 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD). IEEE, 2023. http://dx.doi.org/10.1109/itikd56332.2023.10100150.

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Rapports d'organisations sur le sujet "Cloud-based big data analytics"

Mazorchuk, Mariia S., Tetyana S. Vakulenko, Anna O. Bychko, Olena H. Kuzminska, and Oleksandr V. Prokhorov. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4451.

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This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian

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Cimene, Dr Francis Thaise A. Emerging Technological Trends and Business Process Management: Preparing the Philippines for the Future. Asian Productivity Organization, 2024. https://doi.org/10.61145/dktv2301.

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The Philippine IT-BPM sector plays a vital role in driving economic growth and global competitiveness. This mini-report highlights how emerging technologies such as cloud computing, IoT, and big data analytics are transforming traditional business processes. Grounded in endogenous growth theory, the report emphasizes the impact of innovation and human capital on productivity. Policy recommendations are provided to bolster the nation’s position as a leading outsourcing hub and prepare for future technological advancements.

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Guicheney, William, Tinashe Zimani, Hope Kyarisiima, and Louisa Tomar. Big Data in the Public Sector: Selected Applications and Lessons Learned. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0007024.

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This paper analyzes different ways in which big data can be leveraged to improve the efficiency and effectiveness of government. It describes five cases where massive and diverse sets of information are gathered, processed, and analyzed in three different policy areas: smart cities, taxation, and citizen security. The cases, compiled from extensive desk research and interviews with leading academics and practitioners in the field of data analytics, have been analyzed from the perspective of public servants interested in big data and thus address both the technical and the institutional aspects

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van der Sloot, Bart. The Quality of Life: Protecting Non-personal Interests and Non-personal Data in the Age of Big Data. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64579.

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Under the current legal paradigm, the rights to privacy and data protection provide natural persons with subjective rights to protect their private interests, such as related to human dignity, individual autonomy and personal freedom. In principle, when data processing is based on non-personal or aggregated data or when such data pro- cesses have an impact on societal, rather than individual interests, citizens cannot rely on these rights. Although this legal paradigm has worked well for decades, it is increasingly put under pressure because Big Data processes are typically based indis- crimin

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Mohanty, Subhasish M., Bryan J. Jagielo, William I. Iverson, et al. Online stress corrosion crack and fatigue usages factor monitoring and prognostics in light water reactor components: Probabilistic modeling, system identification and data fusion based big data analytics approach. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1168230.

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Dhulipala, Sravya Sree. Cloud-based data analytics for assessing retrofit needs of residential buildings in rural Iowa. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-372.

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Nasr, Elhami, Tariq Shehab, Nigel Blampied, and Vinit Kanani. Estimating Models for Engineering Costs on the State Highway Operation and Protection Program (SHOPP) Portfolio of Projects. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2365.

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The State Highway Operation and Protection Program (SHOPP) is crucial for maintaining California’s 15,000-mile state highway system, which includes projects like pavement rehabilitation, bridge repair, safety enhancements, and traffic management systems. Administered by Caltrans, SHOPP aims to preserve highway efficiency and safety, supporting economic growth and public safety. This research aimed to develop robust cost-estimating models to improve budgeting and financial planning, aiding Caltrans, the California Transportation Commission (CTC), and the Legislature. The research team collected

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Saz-Carranza, Angel, Oscar Fernández, Marie Vandendriessche, Javier Franco, and Núria Agell. Citizen Perceptions on EU Security and Defence Integration: A Big Data-based Analysis. EsadeGeo. Center for Global Economy and Geopolitics, 2022. https://doi.org/10.56269/202209/asc.

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As European integration has advanced, public opinion on the EU has increasingly come into the spotlight. Yet when it comes to the EU’s Common Security and Foreign Policy (CFSP) and its Common Security and Defence Policy (CSDP), research into acceptability of integration is still “in its infancy” (Biedenkopf et al., 2021, p. 339). Building on an analytical framework of acceptability developed by Michaels & Kissack (2021), this paper sets out to investigate public beliefs and perceptions through big data-based analysis of security-related news published around the world. Our research design

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Papadakis, Stamatios, Арнольд Юхимович Ків, Hennadiy M. Kravtsov, et al. Revolutionizing education: using computer simulation and cloud-based smart technology to facilitate successful open learning. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7375.

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The article presents the proceedings of two workshops: Cloud-based Smart Technologies for Open Education Workshop (CSTOE 2022) and Illia O. Teplytskyi Workshop on Computer Simulation in Education (CoSinE 2022) held in Kyiv, Ukraine, on December 22, 2022. The CoSinE workshop focuses on computer simulation in education, including topics such as computer simulation in STEM education, AI in education, and modeling systems in education. The CSTOE workshop deals with cloud-based learning resources, platforms, and infrastructures, with topics including personalized learning and research environment d

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Nalesso, Mauro, and Pedro Coli. Step by Step Guide: Hydro-BID Manual. Inter-American Development Bank, 2017. http://dx.doi.org/10.18235/0007997.

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The following manual has been prepared with the idea of facilitating the learning process in the use of the Hydro-BID model and the Analytical Hydrographic Database for Latin America and the Caribbean (LAC-AHD). The instructions below are supported by the material distributed in Hydro-BID’s installation package and that is based on the simplified case study of a River basin in the state of Pernambuco, Brazil. By following the instructions you should be able to understand how to set up a simulation in Hydro-BID, how to proceed for the interpolation of climate data, how to calibrate the model an

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