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Articles de revues 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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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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<strong>Background and Aim of Study: </strong>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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<em>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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Bhandari, Adarsh. "Analyzation and Comparison of Cloud Computing and Data Mining Techniques: Big Data and Impact of Blockchain." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 712–21. http://dx.doi.org/10.22214/ijraset.2021.38888.

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Abstract: With the rapid escalation of data driven solutions, companies are integrating huge data from multiple sources in order to gain fruitful results. To handle this tremendous volume of data we need cloud based architecture to store and manage this data. Cloud computing has emerged as a significant infrastructure that promises to reduce the need for maintaining costly computing facilities by organizations and scale up the products. Even today heavy applications are deployed on cloud and managed specially at AWS eliminating the need for error prone manual operations. This paper demonstrate

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Parmar, Jitendra, and Mahendra Singh. "Big Data Analytics in the Cloud: A Survey of Architectures and Technologies." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 3 (2019): 1205–10. http://dx.doi.org/10.61841/turcomat.v10i3.14402.

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In the contemporary generation of burgeoning records, the combination of Big Data analytics with cloud computing has emerged as a paradigm-transferring pressure, facilitating scalable and efficient processing of big datasets. This review paper gives an intensive survey of architectures and technologies that form the bedrock of Big Data analytics inside cloud environments. Tracing the evolution from conventional records processing to dispensed paradigms, the survey explores key architectures, inclusive of Lambda, Kappa, and serverless, shedding mild on their components and scalability attribute

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Middae, Vijaya lakshmi. "Enhancing Cloud Security with AI-Driven Big Data Analytics." American Journal of Engineering and Technology 07, no. 05 (2025): 185–91. https://doi.org/10.37547/tajet/volume07issue05-18.

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Since cloud computing is changing so rapidly, maintaining strong security is now a major issue for companies everywhere. Massive volumes of mixed data are constantly created in cloud environments at every layer, involving virtual machines, containers, storage, identity management and application activities. It is usually not possible for traditional security systems and old monitoring tools to manage vast and changing data flow in real time. Con- ventional methods fail to discover advanced persistent threats, attacks by team members and new vulnerabilities because they do not easily adjust to

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Totade, Prof Mrs Sunita K. "Big Data in Cloud Computing." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 1374–78. http://dx.doi.org/10.22214/ijraset.2024.64858.

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The convergence of Big Data and cloud computing has revolutionized the way organizations process, store, and analyze large datasets. This paper explores the synergistic relationship between these two transformative technologies, highlighting their impact on business operations, decision-making, and innovation. By leveraging cloud platforms, enterprises can harness the scalability, flexibility, and cost-efficiency of distributed computing to manage vast volumes of data generated from diverse sources. Cloud-based big data analytics enables real-time insights, which drive strategic actions, impro

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Ayaburi, Emmanuel Wusuhon Yanibo, Michele Maasberg, and Jaeung Lee. "Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors." Journal of Cases on Information Technology 22, no. 4 (2020): 60–74. http://dx.doi.org/10.4018/jcit.2020100104.

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Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their s

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Mohite, Ganesh, Rushikesh Bhagat, and Roshan Jadhav. "Big Data Analysis using Cloud Computing: Opportunities, Challenges and Applications." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2062–66. https://doi.org/10.22214/ijraset.2025.68353.

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Abstract: Big Data and cloud computing have revolutionized data storage, processing, and analysis, enabling businesses and industries to manage vast volumes of data efficiently. Cloud computing provides scalable infrastructure, cost-effective storage solutions, and real-time analytics capabilities, making it an essential platform for Big Data applications. This study explores the opportunities, challenges, and applications of Big Data analysis in cloud environments, highlighting key technologies such as Hadoop, Spark, and cloud-based data warehousing solutions. The research identifies major ch

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Mrs., Sowmya A. N., Madhavi R. K. Mrs., and Rajani Byakodi Mrs. "Exploring Big Data Analytics: Issues and Tools." IJAPR Journal UGC Indexed 6, no. 2 (2018): 24–34. https://doi.org/10.5281/zenodo.14882169.

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A huge repository of terabytes of data is generated each day from modern information systems and digital technologies such as Internet of Things and cloud computing. Analysis of these massive data requires a lot of effort at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Ad

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Bestak, Dr Robert, and Dr S. Smys. "BIG DATA ANALYTICS FOR SMART CLOUD-FOG BASED APPLICATIONS." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (2019): 74–83. http://dx.doi.org/10.36548/jtcsst.2019.2.001.

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The internet connectivity extended by the internet of things to all the tangible things lying around and used by us in our day today life has convert the devices into smart objects and led to huge set of data generation that holds both the valuable and invaluable information. In order to perfectly handle the information’s generated and mine the valuables from them, the analytics are engaged by the cloud. To have a timely access, most probably the fog services are preferred than the cloud as they bring down the service of the cloud to the user edge and reduces the time complexity in accessing o

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Koppad, Saraswati, Annappa B, Georgios V. Gkoutos, and Animesh Acharjee. "Cloud Computing Enabled Big Multi-Omics Data Analytics." Bioinformatics and Biology Insights 15 (January 2021): 117793222110359. http://dx.doi.org/10.1177/11779322211035921.

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High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis,

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Ara, Affreen, and Aftab Ara. "Cloud for Big Data Analytics Trends." IOSR Journal of Computer Engineering 18, no. 05 (2016): 01–06. http://dx.doi.org/10.9790/0661-1805040106.

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Gill, Sukhpal Singh, Inderveer Chana, and Rajkumar Buyya. "IoT Based Agriculture as a Cloud and Big Data Service." Journal of Organizational and End User Computing 29, no. 4 (2017): 1–23. http://dx.doi.org/10.4018/joeuc.2017100101.

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Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data ana

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Researcher. "DATA WAREHOUSING WITH AMAZON REDSHIFT: REVOLUTIONIZING BIG DATA ANALYTICS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 395–405. https://doi.org/10.5281/zenodo.13270530.

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The article talks about Amazon Redshift, a cutting-edge cloud-based data warehouse that is changing the way big data analytics is done. In it, the architecture, main features, and benefits of Redshift are discussed in detail. Columnar storage, massively parallel processing, and a distributed system design are emphasized. The article discusses how business intelligence, data science, operational analytics, customer analytics, and financial analytics are used in the real world. It also compares and contrasts with other cloud data stores, such as Snowflake and Google BigQuery, pointing out their

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Kushwaha, Ashok, and Dr Kalyan Acharya. "Big Data Analytics in Cloud Computing for Scientific Analytics." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1713–17. http://dx.doi.org/10.22214/ijraset.2022.42636.

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Abstract: Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Keywords: Big data, Analytics, Hadoop, Healthcare, Framework, Methodology.

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Bagheri, Hamid, and Abdusalam Abdullah Shaltooki. "Big Data: challenges, opportunities and Cloud based solutions." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 2 (2015): 340. http://dx.doi.org/10.11591/ijece.v5i2.pp340-343.

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<p class="AbstractText">We are living in an era of information explosion. There are challenges with large and complex amount of data generated every day by social networks, wikis, blogs, emails, traffic system, bridges, airplanes and engine, satellites and weather sensors. 90% of current data in the world has been created in the last two years. Our smart planet becomes more and more intelligent. Besides the challenges posed by such vast amount of data including storage, search, sharing, analysis, and visualization, there are also much opportunities for the world as it becomes more and mo

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Bojkovic, Zoran, and Dragorad Milovanovic. "Mobile cloud analytics in Big data era." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 10 (March 22, 2022): 25–28. http://dx.doi.org/10.37394/232018.2022.10.3.

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Voluminous data are generated from a variety of users and devices and are to be stored and processed in powerful data center. As such, there is a strong demand for building a network infrastructure to gather distributed and rapidly generated data and move them to data center for knowledge discovery. Big data has received considerable attention, because it can mine new knowledge for economic growth and technical innovation. Many research efforts have been directed to big data processing due to its high volume, velocity and variety, referred to as 3V. This paper first describes challenges for bi

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Bofill-De Ros, Xavier, Kevin Chen, Susanna Chen, et al. "QuagmiR: a cloud-based application for isomiR big data analytics." Bioinformatics 35, no. 9 (2018): 1576–78. http://dx.doi.org/10.1093/bioinformatics/bty843.

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Cherekar, Rahul. "Cloud-Based Big Data Analytics: Frameworks, Challenges, and Future Trends." International Journal of AI, BigData, Computational and Management Studies 4 (2023): 35–46. https://doi.org/10.63282/3050-9262.ijaibdcms-v4i1p104.

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Rishabh Rajesh Shanbhag, Rajkumar Balasubramanian, Ugandhar Dasi, Nikhil Singla, and Siddhant Benadikar. "Case Studies and Best Practices in Cloud-Based Big Data Analytics for Process Control." International Journal for Research Publication and Seminar 13, no. 5 (2022): 292–311. http://dx.doi.org/10.36676/jrps.v13.i5.1462.

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In this research paper, case studies and exemplars and lessons learnt in cloud-based big data analytics for process control are reviewed. The paper presents big data, cloud computing and industrial process control system with prospects of enhancing effectiveness, increasing production rates, and effective decision making in the industries. The research in this paper involves a comprehensive literature review of the research topic, and an extension of the analysis to four specific business industries as well as a discussion of architectural elements for cloud-based big data solutions for proces

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Manekar, Amitkumar, and Dr Pradeepini Gera. "Studying Cloud as IaaS for Big Data Analytics : Opportunity, Challenges." International Journal of Engineering & Technology 7, no. 2.7 (2018): 909. http://dx.doi.org/10.14419/ijet.v7i2.7.11094.

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James Watt steam engine revolution was greatest revolution in mankind history in 20th century. In 1776, the first steam engines were installed and working in commercial enterprises. This revolution minimize and make world smaller for human being, now world is connected seamlessly. “Big Data Analytics and Cloud” these two words are second numerous revolutions in 21st century. We are living in an era of information explosion. These two magical terms are nothing but relatively very new and fortunately diverted all market trends to a new era of computation in last decade. As these two emerging tec

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Sachin, Kumar, Prasad K. Krishna, and S. Aithal P. "Banking and Financial Analytics – An Emerging Big Opportunity Based on Online Big Data." International Journal of Case Studies in Business, IT, and Education (IJCSBE) 4, no. 2 (2021): 293–309. https://doi.org/10.5281/zenodo.4451571.

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Business analytics refers to the skills, technology, methods of continuous iterative discovery, and study of past business results. In the banking industry, business analytics can be utilized to the extent that basic banking reporting can be improved with the help of descriptive analytics, predictive analytics, and prescriptive analytics utilizing significant technical developments and the use of big data currently available. The application of business analytics to banking and finance, for both organizations and professionals, is crucial, profitable, and extremely rewarding. Using advanced ma

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Stefanovic, Nenad, Milos Radenkovic, Zorica Bogdanovic, Jelena Plasic, and Andrijana Gaborovic. "Adaptive Cloud-Based Big Data Analytics Model for Sustainable Supply Chain Management." Sustainability 17, no. 1 (2025): 354. https://doi.org/10.3390/su17010354.

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Due to uncertain business climate, fierce competition, environmental challenges, regulatory requirements, and the need for responsible business operations, organizations are forced to implement sustainable supply chains. This necessitates the use of proper data analytics methods and tools to monitor economic, environmental, and social performance, as well as to manage and optimize supply chain operations. This paper discusses issues, challenges, and the state of the art approaches in supply chain analytics and gives a systematic literature review of big data developments associated with supply

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Anandan, R., S. Phani Kumar, K. Kalaivani, and P. Swaminathan. "A survey on big data analytics for enhanced security on cloud." International Journal of Engineering & Technology 7, no. 2.21 (2018): 331. http://dx.doi.org/10.14419/ijet.v7i2.21.12397.

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Cloud based data storage has become a common activity these days. Because cloud storage offers more advantages than normal storage methods those are dynamic access and unlimited storage capabilities for pay and use. But the security of the data outsourced to the cloud is still challenging. The data owner should be capable of performing integrity verification as well as to perform data dynamics of his data stored in the cloud server. Various approaches like cryptographic techniques, proxy based solutions, code based analysis, homomorphic approaches and challenge response algorithms have been pr

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Srikanth, Kandragula. "Big data Analysis in Cloud Computing." European Journal of Advances in Engineering and Technology 6, no. 9 (2019): 82–84. https://doi.org/10.5281/zenodo.13950927.

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The relentless growth of data volume, variety, and velocity, collectively known as big data, presents a complex challenge yet a remarkable opportunity for businesses of all sizes. Cloud computing emerges as a transformative solution, offering the much-needed scalability, flexibility, and cost-effectiveness to effectively analyze and extract valuable insights from big data. This paper delves into the intricate synergy between big data analytics and cloud computing, illuminating the multifaceted benefits it offers and showcasing its real-world applications across diverse industries.

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M., Praveen Kumar, Santhosh Kumar SP., and Ramya G. "Big Data Analytics A Brief Survey." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 2264–68. https://doi.org/10.31142/ijtsrd15617.

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In recent days, the size of the informations generated from modern information systems and digital technologies like IoT and Cloud computing is huge ie. In TB . With this huge sized data, it is quite difficult to analysis and it is in the need of more effects at multiple levels to extract data. Big data analysis is the technique and used both for research and development. The idea of this paper is to give the brief about the big data concepts. Additionally, it will support for the researchers who is doing their research in the area of big data M. Praveen Kumar | SP. Santhosh Kumar | G. Ramya &

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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 (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 tren

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Venkataramana, Jaladurgam. "LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS." ICTACT Journal on Soft Computing 15, no. 2 (2024): 3515–22. http://dx.doi.org/10.21917/ijsc.2024.0490.

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The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unifie

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Miryala, Naresh Kumar, and Divit Gupta. "Big Data Analytics in Cloud – Comparative Study." International Journal of Computer Trends and Technology 71, no. 12 (2023): 30–34. http://dx.doi.org/10.14445/22312803/ijctt-v71i12p107.

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Vaidya, Pranav Vilas, Janaki Meena M, and Syed Ibrahim Sp. "CLOUD-BASED DATA ANALYTICS FRAMEWORK FOR MOBILE APP EVENT ANALYSIS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 207. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19639.

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Mobile analytics studies the behavior of end users of mobile applications and the mobile application itself. These mobile applications, being an important part of the various businesses products, need to be monitored and the usage patterns are to be analyzed. The data collected from these apps can help to drive important business strategies by identifying the usage patterns. Enriching the data with information available from other sources, like sales/service information, provides holistic view about the solution. Thus, here we aim at exploring some set of tools that give capabilities as event

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Wang, Lidong, and Cheryl Ann Alexander. "Big Data Analytics in Healthcare Systems." International Journal of Mathematical, Engineering and Management Sciences 4, no. 1 (2019): 17–26. http://dx.doi.org/10.33889/ijmems.2019.4.1-002.

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Big Data analytics can improve patient outcomes, advance and personalize care, improve provider relationships with patients, and reduce medical spending. This paper introduces healthcare data, big data in healthcare systems, and applications and advantages of Big Data analytics in healthcare. We also present the technological progress of big data in healthcare, such as cloud computing and stream processing. Challenges of Big Data analytics in healthcare systems are also discussed.

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Shah, J. Miah. "A Cloud-based Business Analytics for Supply Chain Decision Support." Journal of Information Sciences and Computing Technologies 4, no. 1 (2015): 274–80. https://doi.org/10.5281/zenodo.3968737.

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Todays’ businesses are required to have control over the big volume of data, transaction information and records that are rapidly generated through various sources, such as networked sensors, Internet sites, smart devices, and industrial machines. This ‘big data’ are significant to process, store, manipulate and communicate for its various strategic and operational purposes. The pattern, growth or declining facts/rates of the big data are important for developing business strategies, improving management and operational business decision making. Although through various indiv

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Et. al., Govindaraju G. N,. "Big Data Analytics Performance Enhancement For Covid-19 Data Using Machine Learning And Cloud." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 5608–14. http://dx.doi.org/10.17762/turcomat.v12i10.5371.

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The exponential rise in software computing, internet and web-services has broadened the horizon for BigData that demands robust and highly efficient analytics system to serve timely and accurate distributed data support. The distributed frameworks with parallelized computing have been found key driving force behind the contemporary BigData analytics systems; however, the lack of optimal data pre-processing, feature sensitive computation and more importantly feature learning makes major at-hand solutions inferior, especially in terms of time and accuracy. Unlike major at hand methods employing

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Alam, Aftab, and Young-Koo Lee. "TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud." Sensors 20, no. 12 (2020): 3581. http://dx.doi.org/10.3390/s20123581.

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In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed

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Shankar, Venky. "Big Data and Analytics in Retailing." NIM Marketing Intelligence Review 11, no. 1 (2019): 36–40. http://dx.doi.org/10.2478/nimmir-2019-0006.

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AbstractBig data are taking center stage for decision-making in many retail organizations. Customer data on attitudes and behavior across channels, touchpoints, devices and platforms are often readily available and constantly recorded. These data are integrated from multiple sources and stored or warehoused, often in a cloud-based environment. Statistical, econometric and data science models are developed for enabling appropriate decisions. Computer algorithms and programs are created for these models. Machine learning based models, are particularly useful for learning from the data and making

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Venkata Siva Reddy, D., and R. Vasanth Kumar Mehta. "Cloud based computational intelligence approaches to machine learning and big data analytics: literature survey." International Journal of Engineering & Technology 7, no. 1.9 (2018): 186. http://dx.doi.org/10.14419/ijet.v7i1.9.9817.

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Today there are many sources through which we can access information from internet and based on the dependency now there is an over flow of data either in refined form or unrefined form. Handling large information is a complicated task. It has to overcome many challenges. There are some challenges like drawing useful information from undefined patterns which we can overcome by using data mining techniques but certain challenges like scalability, easy accessing of large data, time, or cost areto be handled in better sense.Machine learning helps in learning patterns from data automatically and c

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Islam, Ashraful. "DATA GOVERNANCE AND COMPLIANCE IN CLOUD-BASED BIG DATA ANALYTICS: A DATABASE-CENTRIC REVIEW." ACADEMIC JOURNAL ON SCIENCE, TECHNOLOGY, ENGINEERING & MATHEMATICS EDUCATION 1, no. 01 (2024): 53–71. http://dx.doi.org/10.69593/ajieet.v1i01.122.

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This study examines the evolving landscape of data governance in cloud-based big data analytics, emphasizing the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), and blockchain. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 120 articles were systematically reviewed to explore how organizations are addressing the challenges of managing large-scale, decentralized datasets while ensuring regulatory compliance and data security. The findings reveal that AI and ML are increasingly being u

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Abdelhafez, Hoda Ahmed. "Big Data Technologies and Analytics." International Journal of Business Analytics 1, no. 2 (2014): 1–17. http://dx.doi.org/10.4018/ijban.2014040101.

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The internet era creates new types of large and real-time data; much of those data are non-standard such as streaming and sensor-generated data. Advanced big data technologies enable organizations to extract insights from sophisticated data. Volume, variety and velocity represent big data challenges, which cause difficulties in capture, storage, search, sharing, analysis and visualization. Therefore, technologies like No-SQL, Hadoop and cloud computing used to extract value from large volumes and a wide variety of data to discover business needs. This article's goal is to focus on the challeng

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Pham, Linh Manh, Truong-Thang Nguyen, and Tien-Quang Hoang. "Towards an Elastic Fog-Computing Framework for IoT Big Data Analytics Applications." Wireless Communications and Mobile Computing 2021 (August 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/3833644.

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IoT applications have been being moved to the cloud during the last decade in order to reduce operating costs and provide more scalable services to users. However, IoT latency-sensitive big data streaming systems (e.g., smart home application) is not suitable with the cloud and needs another model to fit in. Fog computing, aiming at bringing computation, communication, and storage resources from “cloud to ground” closest to smart end-devices, seems to be a complementary appropriate proposal for such type of application. Although there are various research efforts and solutions for deploying an

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Ashraf, Syed Ziaurrahman. "AI-Driven Data Preparation: The Key to Unlocking Cloud-Based Analytics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem23999.

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The rapid adoption of cloud-based analytics has revolutionized data-driven decision-making across industries. Cloud- based analytics has transformed how businesses make decisions by leveraging vast amounts of data. However, preparing data for analysis—such as cleaning, transforming, and organizing it—can be a complicated and time- consuming process. AI-driven data preparation (AIDP) is a solution that automates these steps, reducing the time and effort needed to prepare data while improving its quality. This paper explains the importance of AI-driven data preparation, discusses how it works, a

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Chinnathambi, Jinesh Kumar. "Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration." European Journal of Computer Science and Information Technology 12, no. 6 (2024): 15–23. http://dx.doi.org/10.37745/ejcsit.2013/vol12n61523.

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Amplifying the utilization of big data in healthcare analytics through cloud and Snowflake migration presents a significant opportunity to enhance data-driven insights and decision-making in the healthcare sector. This migration makes it easier to move large amounts of healthcare data to the cloud. Applications deployed in could are scalable for in-depth analysis in Health Care industry. The cloud is becoming more popular for storing data and running applications because it can easily grow with your needs, requires little to no management, improves security, and offers budget flexibility. The

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Simmhan, Yogesh, Saima Aman, Alok Kumbhare, et al. "Cloud-Based Software Platform for Big Data Analytics in Smart Grids." Computing in Science & Engineering 15, no. 4 (2013): 38–47. http://dx.doi.org/10.1109/mcse.2013.39.

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