Journal articles: 'Analytics Computing'

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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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Papageorgiou, Nikos, Yiannis Verginadis, Dimitris Apostolou, and Gregoris Mentzas. "Fog computing context analytics." IEEE Instrumentation & Measurement Magazine 22, no. 6 (2019): 53–59. http://dx.doi.org/10.1109/mim.2019.8917904.

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KIM, JENNIFER, DAVID A. OSTROWSKI, HIROSHI YAMAGUCHI, and PHILLIP C. Y. SHEU. "SEMANTIC COMPUTING AND BUSINESS INTELLIGENCE." International Journal of Semantic Computing 07, no. 01 (2013): 87–117. http://dx.doi.org/10.1142/s1793351x13500013.

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With rapidly expanding data collections becoming increasingly available, the application of Semantic Computing has become imperative to leverage this resource for industrial applications. This paper presents a survey of Semantic Computing in the area of Business Intelligence. We examine semantic analytical techniques and tools as applied for prediction analysis and decision support. We also define the role of Semantic Computing as applied in the context of Data Mining, Text Mining and Big Data Analytics. Additionally, we describe how business data is queried with Structured Natural Language as well as the use of On-Line Analytic Processing.

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S Pathak, Shantanu, and D. Rajeswara Rao. "Reservoir Computing for Healthcare Analytics." International Journal of Engineering & Technology 7, no. 2.32 (2018): 240. http://dx.doi.org/10.14419/ijet.v7i2.32.15576.

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In this data age tools for sophisticated generation and handling of data are at epitome of usage. Data varying in both space and time poses a breed of challenges. Challenges they possess for forecasting can be well handled by Reservoir computing based neural networks. Challenges like class imbalance, missing values, locality effect are discussed here. Additionally, popular statistical techniques for forecasting such data are discussed. Results show how Reservoir Computing based technique outper-forms traditional neural networks.

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Brahmaji Kanagarla, Krishna Prasanth. "Quantum Computing For Data Analytics." International Journal of All Research Education and Scientific Methods 11, no. 05 (2023): 3389–94. http://dx.doi.org/10.56025/ijaresm.2024.1105233389.

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The focus of the study lies in the transformative power of quantum computing with regard to finance, health and artificial intelligence. New algorithms enabled but quantum allow for previously unattainable speeds in complex processing. The current work is gone into references of some of these quantum algorithms among those put forward by Grover and Shor besides the assessment of the impact of each in enhancing analytics. Major challenges in term of practical implementation in real applications such as scalability and error correction-are reviewed. Future directions of research would then indicate refinement in such a way that these systems can ensure reliability and industry integration. The research identifies the heights of promise quantum computing holds for innovation to change the face of data analytics as a leading force.

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Earley, Seth. "Cognitive Computing, Analytics, and Personalization." IT Professional 17, no. 4 (2015): 12–18. http://dx.doi.org/10.1109/mitp.2015.55.

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Aafreen, Qureshi, and Gaurav Indra Dr. "Machine Learning Driven Edge Analytics for Healthcare: Problems, Difficulties, Future Directions, and Applications-A Review." International Journal of Innovative Science and Research Technology 7, no. 12 (2023): 1427–41. https://doi.org/10.5281/zenodo.7525315.

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With the use of edge technology, cloud resources (particularly computing, storage, and network) will be made available in close proximity to edge devices, or smart gadgets where data is generated and consumed. Edge computing and edge analytics are two new ideas in edge technology that have emerged as a result of computer and application integration in edge devices. To examine the information produced through edge gadgets, edge analytics employs a number of methods or algorithms. The development of edge analytics has made the edge gadgets a whole set. Edge analytics is currently unable to fully accomodate the analytic methodologies. Due to several limitations like a low power supply, a tiny memory, a lack of resources, etc., the edge gadgets cannot conduct complex and refined analytic algorithms. The purpose of this paper is to give a thorough explanation of edge analytics. The following are the paper's main contributions: a detailed description of the differences among the three edge technology ideas of edge gadgets, edge computing, and edge analytics, as well as their problems. The article also examines how edge analytics are being used in numerous industries, including retail, agriculture, industry, and healthcare, to solve a variety of issues. Additionally, the research papers based on cutting-edge analytics are thoroughly examined in this article to analyse the current problems, new difficulties, research prospects, as well as utilizations.

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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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Oluwole Temidayo Modupe, Aanuoluwapo Ayodeji Otitoola, Oluwatayo Jacob Oladapo, et al. "REVIEWING THE TRANSFORMATIONAL IMPACT OF EDGE COMPUTING ON REAL-TIME DATA PROCESSING AND ANALYTICS." Computer Science & IT Research Journal 5, no. 3 (2024): 693–702. http://dx.doi.org/10.51594/csitrj.v5i3.929.

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Edge computing has emerged as a pivotal paradigm shift in the realm of data processing and analytics, revolutionizing the way organizations handle real-time data. This review presents a comprehensive review of the transformational impact of edge computing on real-time data processing and analytics. Firstly, the review delves into the fundamental concepts of edge computing, elucidating its architectural framework and highlighting its distinct advantages over traditional cloud-centric approaches. By distributing computational resources closer to data sources, edge computing mitigates latency issues and enhances responsiveness, thereby enabling real-time data processing at the edge. Furthermore, this review explores how edge computing facilitates the seamless integration of analytics capabilities into edge devices, empowering organizations to derive actionable insights at the source of data generation. Leveraging advanced analytics algorithms, such as machine learning and artificial intelligence, edge computing enables autonomous decision-making and predictive analytics in real time, fostering innovation across diverse industry verticals. Moreover, the review examines the transformative implications of edge computing on various sectors, including healthcare, manufacturing, transportation, and smart cities. By enabling localized data processing and analytics, edge computing enhances operational efficiency, ensures data privacy and security, and unlocks new opportunities for business optimization and value creation. This review underscores the profound impact of edge computing on real-time data processing and analytics, revolutionizing the way organizations harness data to drive informed decision-making and gain competitive advantage in today's dynamic business landscape. As edge computing continues to evolve, its transformative potential is poised to redefine the future of data-driven innovation and digital transformation. Keywords: Edge, Computing, Analytics, Data, Impact, Review.

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Singh, Kiran Deep, Prabh Deep Singh, Rohan Verma, and Harsh Taneja. "Optimizing health data analytics in fog computing using hyperparameter tuning and grid search." Journal of Information and Optimization Sciences 45, no. 2 (2024): 429–38. http://dx.doi.org/10.47974/jios-1560.

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The integration of fog computing with health data analytics signifies a paradigm shift in the field of healthcare, offering the potential for streamlined and prompt analysis of patient welfare. The increasing volume of health data necessitates the development of efficient analytical models in fog computing settings. The objective of this research is to examine the integration of fog computing and health data analytics, specifically emphasizing the utilization of hyperparameter tuning and grid search techniques to enhance optimization approaches. Hyperparameter tuning and grid search are two techniques utilized in machine learning to optimize the performance of models. These methods are employed in the context of health data analytics inside fog computing with the objective of improving accuracy, reducing latency, and enhancing resource efficiency. Our research endeavors to provide significant contributions to the advancement of adaptable and responsive healthcare systems, therefore promoting enhanced patient outcomes in the era of data-driven decision-making.

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Ayyasamy, S. "Contemporary High-Performance Computing for Big Data Applications." December 2023 5, no. 4 (2023): 375–84. http://dx.doi.org/10.36548/jitdw.2023.4.004.

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High-performance computing (HPC) involves leveraging parallel data processing to enhance computer performance and handle difficult tasks. HPC meets these aims by pooling computing capacity, enabling efficient, reliable, and prompt execution of even complex programs according to user demands and expectations. The rapid growth of HPDA in many sectors has led to the extension of the HPC market into new territory. HPC as well as Big Data systems differ not just in terms of technology but also in ecosystems. Extensive research in this sector has led to the emergence of various Big Data analytics models in recent years. As Big Data analytics spreads across several fields, new challenges about the usefulness of analytical paradigms also emerge. This article discusses the key analytical models, as well as the difficulties and challenges associated with high-performance data analytics. This research work aims to identify the factors influencing the integration of HPC with big data, including present and future trends. The study also proposes an architecture for big data with HPC convergence based on design principles.

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Prakash, Saurav, Amirhossein Reisizadeh, Ramtin Pedarsani, and Amir Salman Avestimehr. "Coded Computing for Distributed Graph Analytics." IEEE Transactions on Information Theory 66, no. 10 (2020): 6534–54. http://dx.doi.org/10.1109/tit.2020.2999675.

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Yu, Hsiang-Fu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, and Inderjit Dhillon. "Nomadic Computing for Big Data Analytics." Computer 49, no. 4 (2016): 52–60. http://dx.doi.org/10.1109/mc.2016.116.

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Sukhwani, Bharat, Hong Min, Mathew Thoennes, et al. "Database Analytics: A Reconfigurable-Computing Approach." IEEE Micro 34, no. 1 (2014): 19–29. http://dx.doi.org/10.1109/mm.2013.107.

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Anandakumar, H., R. Arulmurugan, and Chow Chee Onn. "Big Data Analytics for Sustainable Computing." Mobile Networks and Applications 24, no. 6 (2019): 1751–54. http://dx.doi.org/10.1007/s11036-019-01393-6.

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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 becomes universal, harnessing the potential of big data analytics from any location with an internet connection. However, challenges such as data security and privacy, latency issues, and the cost of long-term storage and complex analytics tasks in the cloud need to be addressed. Robust security measures, efficient data management strategies, and adherence to compliance standards are necessary to ensure the safe and effective utilization of big data within cloud environments.

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Popa, Raluca Ada. "Confidential Computing or Cryptographic Computing?" Queue 22, no. 2 (2024): 108–32. http://dx.doi.org/10.1145/3664295.

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Secure computation via MPC/homomorphic encryption versus hardware enclaves presents tradeoffs involving deployment, security, and performance. Regarding performance, it matters a lot which workload you have in mind. For simple workloads such as simple summations, low-degree polynomials, or simple machine-learning tasks, both approaches can be ready to use in practice, but for rich computations such as complex SQL analytics or training large machine-learning models, only the hardware enclave approach is at this moment practical enough for many real-world deployment scenarios.

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Nida, Bhanu Raju. "From Classical to Quantum: The Future of Advanced Analytics with Quantum Computing." International Journal of Multidisciplinary Research and Growth Evaluation. 6, no. 2 (2025): 587–93. https://doi.org/10.54660/.ijmrge.2025.6.2.587-593.

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The rapid growth of complex analytics has moved from classical statistical methods to the AI-based systems using large data sets for predictive and prescriptive decision making. Although the traditional computing systems have greatly improved decision-making abilities, they have their limitations in the complex and large-scale data collection and real time processing and analysis. Quantum computing is a major step forward in the enhancement of computational capability with the help of principles such as superposition and entanglement to perform several calculations at a time. This paper explores the integration of quantum computing with data science and the prospect of quantum computing outperforming the classical computing in optimization, machine learning, predictive analytics and natural language processing. Classical and quantum analytics models are compared, quantum computing platforms are evaluated through experimental testing, and computational efficiency is compared through benchmarking. The benefits of quantum enhanced analytics however are illustrated through the examination of empirical case studies from the finance, healthcare, supply chain and AI marketing sectors. Nevertheless, there are challenges including hardware limitations, algorithmic constraints, and integration barrier. Quantum computing is expected to redefine analytics by offering enhanced computational speed and accuracy. This research highlights the need for more efficient and scalable quantum algorithms, quantum error correction, and quantum-classical hybrid models to make the quantum theory applicable to practical use in business intelligence and big data analytics.

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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 distributed computing, machine learning, and artificial intelligence. These technologies facilitate the extraction of meaningful patterns and trends from large, complex datasets in real time. Key cloud-based platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, offer a range of tools and services designed to simplify the deployment and management of big data analytics. Challenges remain in areas such as data security, privacy, and governance, which are critical for maintaining the integrity and confidentiality of sensitive information. Additionally, optimizing the performance and cost-efficiency of big data analytics in the cloud requires careful planning and management. This abstract highlights the critical role of cloud computing in advancing big data analytics, emphasizing its potential to transform industries through enhanced data-driven strategies while acknowledging the associated challenges and considerations.

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Machii, Jackson, Julius Murumba, and Elyjoy Micheni. "Educational Data Analytics and Fog Computing in Education 4.0." Open Journal for Information Technology 6, no. 1 (2023): 47–58. http://dx.doi.org/10.32591/coas.ojit.0601.04047m.

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Universities are generating massive amounts of educational data. Most universities are now focusing on how to harness that data to optimize and visualize it to provide better and more extended education services. Given this scenario, a literature review was used to conduct this study guided by the following objectives: (1) Assess suitable fog computing and educational data analytics architectures; (2) Examine the opportunities offered by fog computing and educational data analytics; (3) Investigate fog computing and educational data analytics challenges; and (4) Examine disruptions and future directions of these technologies in Education 4.0. The study concludes that institutions must use integrated data analytics techniques and distributed technology systems to make decisions about administration, resource allocation, student retention, performance, and improvement strategies. The study also identified the challenges of using fog computing and educational data analytics and concludes that education 4.0 is a learning style that is aligned with the fourth industrial revolution, requiring transformational learning readiness.

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Pedrycz, Witold. "Granular computing for data analytics: a manifesto of human-centric computing." IEEE/CAA Journal of Automatica Sinica 5, no. 6 (2018): 1025–34. http://dx.doi.org/10.1109/jas.2018.7511213.

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Dr., Sumita U. Sharma. "A COMPREHENSIVE SURVEY ON BIG DATA ANALYTICS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [NC-Rase 18] (November 15, 2018): 39–42. https://doi.org/10.5281/zenodo.1488685.

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In the past few years, there has been a rapid growth in innovative ideas and developments in the field of technology. The world has ushered into many new computing paradigms like pervasive computing, cloud computing, embedded programming, Internet of Things and many. But amongst all of them Data Analytics enjoys a special place. The term big data and data analytics has become the modern buzz word. Not only there are many myths and hypes about this new technology, it poses many challenges and issues. The current paper presents a detailed and comprehensive study on the data analytics, the techniques used with the challenges and the future of this multifaceted modern technology.

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Akoh Atadoga, Ogugua Chimezie Obi, Femi Osasona, et al. "QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS." Computer Science & IT Research Journal 5, no. 2 (2024): 498–517. http://dx.doi.org/10.51594/csitrj.v5i2.794.

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This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.

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Sharma, Aryaman. "QUANTUM COMPUTING: A REVIEW ON BIG DATA ANALYTICS AND DATA SECURITY." International Research Journal of Computer Science 9, no. 4 (2022): 96–100. http://dx.doi.org/10.26562/irjcs.2021.v0904.005.

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The world's most difficult problem during the preceding decade was the big data problem. The big data challenge refers to the fact that data is growing at a much faster rate than computational rates. People, as well as virtually all organizations and scientific institutions, are keeping a rising amount of data as the cost of storage falls every day. Large amounts of data are generated by social activities, scientific inquiries, biological discoveries, and sensor devices. Big data is beneficial to society and economy, but it also poses challenges to scientific communities. Traditional tools, machine learning algorithms, and methodologies cannot handle, manage, or analyze large amounts of data. Quantum computing, in the realm of Big Data, allows businesses to collect and analyze large volumes of data quickly using quantum algorithms. Separate data sets may be detected, analyzed, integrated, and diagnosed with far greater ease. To find patterns, all of the items of a large database can be studied at the same time. As a result, it may be years before quantum computing makes its way into most businesses or becomes a common data analytics tool. Quantum computing will still be a relatively new technology in 2021. Machine learning algorithms are currently improving thanks to breakthroughs in quantum computing technology. There's still a lot to learn about quantum computing's capabilities and the implications of such a strong technology.

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Mumtaz, Topiwale, Priyanka, and Aishwarya. "Sky Insight Unleashing the Power of Cloud Analytics." Recent Trends in Cloud Computing and Web Engineering 5, no. 2 (2023): 20–28. https://doi.org/10.5281/zenodo.7912019.

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Cloud computing is a technology that has transformed the way businesses store and process data. It allows users to get data and applications over the internet, without the need for on-premises infrastructure. With cloud computing, businesses can store data in remote servers controlled by third-party providers, which erase the need for costly hardware and maintenance. Computing has also allowed the development of cloud analytics, which is the use of cloud-based tools and platforms to perform data analysis. Cloud analytics has transformed the way businesses approach data analysis, as it provides a range of merits over traditional on-premises analytics. Some of the merits of cloud computing include scalability, cost-effectiveness, flexibility, and increased productivity. By leveraging cloud computing, businesses can scale their framework to satisfy their reconstructing demands, pay only for what they use, and access their data and applications from anywhere. It has had an obvious Bang on the way businesses store and process data, and cloud analytics has arisen as a powerful tool for businesses looking to acquire insights from their data.

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Elmqvist, Niklas. "Data Analytics Anywhere and Everywhere." Communications of the ACM 66, no. 12 (2023): 52–63. http://dx.doi.org/10.1145/3584858.

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K, Daniel Raj, Geowin Christosingh R, Josephine Monisha R, and Jeyapretta Emima J. "Fog and Mist Computing for Real-Time IOT Analytics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–8. https://doi.org/10.55041/ijsrem44813.

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Abstract -This paper presents a novel approach to real- time Internet of Things (IoT) analytics by integrating Fog and Mist Computing. By shifting data processing closer to the source, at the network edge, this methodology reduces latency and enhances resource efficiency. The study proposes an innovative architecture that utilizes advanced algorithms to enable real-time data analysis, facilitating faster decision-making without overburdening centralized cloud systems. The results demonstrate a significant improvement in system performance, addressing common challenges such as network congestion, bandwidth limitations, and energy consumption. By decentralizing computing tasks, the approach increases the scalability and responsiveness of IoT systems, particularly in sectors like healthcare, smart cities, and industrial automation. This work highlights the practical advantages of Fog and Mist Computing in optimizing large-scale IoT networks, offering a promising solution for real-time data processing in resource-constrained environments. Key Words: Fog Computing, Mist Computing, IoT Analytics, Real-Time Processing, Edge Computing, Decentralized Systems.

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Zheng, Yixian, Wenchao Wu, Yuanzhe Chen, Huamin Qu, and Lionel M. Ni. "Visual Analytics in Urban Computing: An Overview." IEEE Transactions on Big Data 2, no. 3 (2016): 276–96. http://dx.doi.org/10.1109/tbdata.2016.2586447.

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Bonacorsi, D., V. Kuznetsov, N. Magini, A. Repečka, and E. Vaandering. "Exploiting analytics techniques in CMS computing monitoring." Journal of Physics: Conference Series 898 (October 2017): 092030. http://dx.doi.org/10.1088/1742-6596/898/9/092030.

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Deng, Yang, Tao Han, and Nirwan Ansari. "FedVision: Federated Video Analytics With Edge Computing." IEEE Open Journal of the Computer Society 1 (2020): 62–72. http://dx.doi.org/10.1109/ojcs.2020.2996184.

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Hundhausen, C. D., D. M. Olivares, and A. S. Carter. "IDE-Based Learning Analytics for Computing Education." ACM Transactions on Computing Education 17, no. 3 (2017): 1–26. http://dx.doi.org/10.1145/3105759.

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Malkoochi, Ramchander. "Confidential Computing for Privacy-Preserving Fraud Analytics." European Journal of Computer Science and Information Technology 13, no. 24 (2025): 115–228. https://doi.org/10.37745/ejcsit.2013/vol13n24115228.

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Confidential computing represents a transformative paradigm in fraud analytics, providing robust protection for sensitive financial data throughout the processing lifecycle. By leveraging Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV, financial institutions can analyze transaction patterns, detect anomalies, and collaborate across organizational boundaries while maintaining data confidentiality. The technology addresses the fundamental tension between effective fraud detection and privacy protection through hardware-based isolation mechanisms that secure data even during computation. This comprehensive overview explores how confidential computing enhances fraud analytics through privacy-preserving machine learning, secure multi-party computation, and cryptographic integrity guarantees. The implementation pathways through cloud platforms enable financial organizations to deploy these solutions within existing infrastructure while acknowledging the challenges related to performance, scalability, and hardware constraints as these technologies mature alongside complementary approaches like homomorphic encryption and blockchain integration, confidential computing positions itself as the cornerstone of privacy-preserving fraud analytics in an increasingly data-sensitive financial ecosystem.

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Bhatia, Jitendra, Kiran Italiya, Kuldeepsinh Jadeja, et al. "An Overview of Fog Data Analytics for IoT Applications." Sensors 23, no. 1 (2022): 199. http://dx.doi.org/10.3390/s23010199.

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With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends.

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Kamini Murugaboopathy. "Leveraging Cloud Computing for Real-Time Marketing Analytics: A Technical Perspective." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1229–43. https://doi.org/10.32628/cseit25112450.

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This article examines how cloud computing has revolutionized marketing analytics by enabling real-time data processing and decision-making capabilities previously unattainable with traditional on-premise systems. It presents a comprehensive technical analysis of cloud-native architectures for marketing analytics, detailing the multi-layered framework that spans from data ingestion through to action delivery. The article explores how organizations have overcome the historical limitations of traditional analytics environments—including data silos, batch processing constraints, and limited computational resources—through the implementation of cloud-based platforms. The technical architecture is dissected across its primary components: the data ingestion layer that captures customer interactions as they occur; the processing layer that transforms raw data into actionable insights within milliseconds; specialized storage technologies optimized for analytical workloads; the analytics layer with its visualization and machine learning capabilities; and the action layer that enables immediate customer engagement. The article further addresses critical implementation considerations related to performance optimization, scalability, data governance, and cost management. Through examination of real-world applications like dynamic audience segmentation, predictive customer lifetime value modeling, and personalized content orchestration, it demonstrates how cloud technologies deliver substantial competitive advantages. The article concludes by exploring emerging trends at the intersection of artificial intelligence and cloud computing that will shape the next generation of marketing analytics capabilities.

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Sun, Zhaohao. "Similarity Intelligence: Similarity Based Reasoning, Computing, and Analytics." Journal of Computer Science Research 5, no. 3 (2023): 1–14. http://dx.doi.org/10.30564/jcsr.v5i3.5575.

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Similarity has been playing an important role in computer science, artificial intelligence (AI) and data science. However, similarity intelligence has been ignored in these disciplines. Similarity intelligence is a process of discovering intelligence through similarity. This article will explore similarity intelligence, similarity-based reasoning, similarity computing and analytics. More specifically, this article looks at the similarity as an intelligence and its impact on a few areas in the real world. It explores similarity intelligence accompanying experience-based intelligence, knowledge-based intelligence, and data-based intelligence to play an important role in computer science, AI, and data science. This article explores similarity-based reasoning (SBR) and proposes three similarity-based inference rules. It then examines similarity computing and analytics, and a multiagent SBR system. The main contributions of this article are: 1) Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence. 2) Similarity-based reasoning, computing and analytics can be used to create similarity intelligence. The proposed approach will facilitate research and development of similarity intelligence, similarity computing and analytics, machine learning and case-based reasoning.

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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 demonstrates about certain cloud computing tools and techniques present to handle big data and processes involved while extracting this data till model deployment and also distinction among their usage. It will also demonstrate, how big data analytics and cloud computing will change methods that will later drive the industry. Additionally, a study is presented later in the paper about management of blockchain generated big data on cloud and making analytical decision. Furthermore, the impact of blockchain in cloud computing and big data analytics has been employed in this paper. Keywords: Cloud Computing, Big Data, Amazon Web Services (AWS), Google Cloud Platform (GCP), SaaS, PaaS, IaaS.

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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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Singh Bhathal, Gurjit. "Challenges and Opportunities in Mobile Cloud Computing for Big Data Analytics." International Journal of Science and Research (IJSR) 11, no. 1 (2022): 1611–15. http://dx.doi.org/10.21275/es24424211225.

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Ma, Xiao, Zie Wang, Sheng Zhou, Haoyu Wen, and Yin Zhang. "Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing." Wireless Communications and Mobile Computing 2018 (July 3, 2018): 1–16. http://dx.doi.org/10.1155/2018/3928080.

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It is entering an era of big data, which facilitated great improvement in various sectors. Particularly, assisted by wireless communications and mobile computing, mobile devices have emerged with a great potential to renovate the healthcare industry. Although the advanced techniques will make it possible to understand what is happening in our body more deeply, it is extremely difficult to handle and process the big health data anytime and anywhere. Therefore, data analytics and mobile computing are significant for the healthcare systems to meet many technical challenges and problems that need to be addressed to realize this potential. Furthermore, the advanced healthcare systems have to be upgraded with new capabilities such as machine learning, data analytics, and cognitive power for providing human with more intelligent and professional healthcare services. To explore recent advances and disseminate state-of-the-art techniques related to data analytics and mobile computing on designing, building, and deploying novel technologies, to enable intelligent healthcare services and applications, this paper presents the detailed design for developing intelligent healthcare systems assisted by data analytics and mobile computing. Moreover, some representative intelligent healthcare applications are discussed to show that data analytics and mobile computing are available to enhance the performance of the healthcare services.

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Lv, Zhihan, Liang Qiao, Sahil Verma, and Kavita. "AI-enabled IoT-Edge Data Analytics for Connected Living." ACM Transactions on Internet Technology 21, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3421510.

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As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

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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 machine learning for BigData analytics, in this paper the key emphasis was made on improving pre-processing, low-dimensional semantic feature extraction and lightweight improved machine learning based feature learning for BigData analytics. Noticeably, the proposed model hypothesizes that an analytics solution with BigData characteristics must have the potential to process humongous, heterogenous, unstructured and multi-dimensional features to yield time-efficient and accuracy analytical outputs. In this reference, we proposed a state-of-art new and robust BigData analytics model, specially designed for Spark distributed framework. To process analytical task our proposed model at first employs tokenization, followed by Word2Vec based semantic feature extraction using CBOW and N-Skip-Gram methods. Our proposed model was found more effective with Skip-Gram Word2Vec feature extraction. Simulation results with a publicly available COVID-19 data exhibited better performance than existing K-Means based MapReduce distributed data frameworks.

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Monu, Sharma. "Workday Prism Analytics: Leveraging the Power of One for Comprehensive Data Management." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 2 (2021): 1–9. https://doi.org/10.5281/zenodo.14770909.

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This paper examines the integration of Prism Analytics with Workday, emphasizing the "Power of One" concept in the context of cloud computing. By unifying data acquisition, preparation, transformation, and publishing, organizations can leverage this application to harness the full potential of their data. Workday provides a robust foundation with its comprehensive human capital management and financial data, while Prism Analytics enhances analytical capabilities through advanced data processing tools. The paper discusses the streamlined workflows for data ingestion and transformation, highlighting best practices for ensuring data quality and integrity. Furthermore, it explores how the cloud-based architecture facilitates real-time data access and collaboration across departments, enabling organizations to publish actionable insights efficiently. Ultimately, this prism empowers decision-makers to make informed, strategic choices in a competitive landscape, demonstrating the critical role of cloud computing in modern data analytics.

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Researcher. "CLOUD COMPUTING FOR EFFICIENT IMPLEMENTATION OF BUSINESS ANALYTICS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 475–84. https://doi.org/10.5281/zenodo.13845994.

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With exponential growth in data collection of different variety, volatility and with high velocity, we are ending up with huge data. Managing, storing, processing and understanding the hidden details in that data, has become very important for businesses and given rise to the field of business analytics. In cloud computing with the help of cloud services, we can setup resources efficiently, easily and in very less cost. Today, we have 3 large cloud providers Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP). This paper pro- vides a comprehensive comparison of different services provided by these cloud providers for different stages and use cases of business analytics. It delves into core services, cost, performance and implementation aspects. It also explores some common use cases of business analytics and try to provide a comparison between these services with a goal to provide clarity to users about service selection. Key- words: business analytics, cloud computing, cloud service providers, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP).

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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 a certain role in the future.

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Sai, Sandeep Ogety. "Enhancing Cloud Security Governance with AI and Data Analytics." European Journal of Advances in Engineering and Technology 8, no. 7 (2024): 132–42. https://doi.org/10.5281/zenodo.14274546.

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The group of real-world physical devices like sensors, machines, vehicles and various “things” connected to Internet is called as Internet of things (IoT). The major challenge in IoT is that  it is fully dependent on the cloud for all kinds of computation, which leads to high latency in the IoT devices. To overcome this latency issue, the Serverless edge computing and AI approaches were introduced newline. Serverless edge computing allows moving the data goverence and managing closer to the Serverless edge of the device. ICT’s three pillars namely computing, network and storage faces some challenges in terms of goverence and structuring the data while using formal Cloud computing methods. To propose a framework on IoT devices data by combining two things which is mainly focused on IoT data goverence and data security goverence goverence. To design modified auto-encoder algorithms (AI) for goverence of data in Serverless edge computing architecture. To investigate the present scenario of the data accessing techniques, then to design an effective auto-encoder model to process the huge amount of raw data generated from IoT devices time-to- time (Transforming data to Serverless edge) in the Serverless edge Computing. To consider different types of attacks on IoT data, to investigate the different policies of security and to design a model for Access Control for IoT data by considering the above important processes which can solve the current problems in IoT data access and security. In the performance analysis, Latency minimization, Network Management, Cost Optimization, Data Management, Energy Management, and Resource Management are analysed at the service level and Serverless edge computing based IoT security challenges and self-protection system for IoT specifically in detection, prediction and response mechanisms discussed.

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Barik, Rabindra K., Rojalina Priyadarshini, Harishchandra Dubey, Vinay Kumar, and Kunal Mankodiya. "FogLearn." International Journal of Fog Computing 1, no. 1 (2018): 15–34. http://dx.doi.org/10.4018/ijfc.2018010102.

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Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.

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Fatima, Syeda Alishba, Syeda Faiza Nasim, and Saad Ahmed. "Enhancing Agricultural Operations: Big Data Analytics Using Distributed and Parallel Computing." International Journal of Emerging Engineering and Technology 2, no. 2 (2023): 1–7. http://dx.doi.org/10.57041/v3jj9f69.

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This comprehensive research investigates using distributed and parallel computing for big data analytics in agriculture to improve farming operations' sustainability, efficiency, and innovation. The paper emphasizes how big data analytics, cloud computing, and parallel distributed processing can revolutionize the agricultural industry. The research objectives include investigating the benefits and limitations of big data analytics in precision farming and crop monitoring, identifying the constraints of integrating big data analytics in agriculture and investigating the role of frameworks such as Hadoop and Spark in processing and analyzing agricultural data for informed decision-making and optimized farming operations. The methodology used in the paper is a literature review, which draws on various sources to provide insights into the topic matter. The findings indicate that big data analytics can considerably improve precision farming and crop monitoring; nevertheless, there are hurdles to incorporating big data analytics in agriculture, such as data privacy and security concerns. According to the study, frameworks such as Hadoop and Spark are crucial in processing and analyzing agricultural data for informed decision-making and better farming operations. Overall, this study offers useful insights into the possibilities of big data analytics and distributed and parallel computing in revolutionizing the agriculture industry.

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Baciu, George, Yungzhe Wang, and Chenhui Li. "Cognitive Visual Analytics of Multi-Dimensional Cloud System Monitoring Data." International Journal of Software Science and Computational Intelligence 9, no. 1 (2017): 20–34. http://dx.doi.org/10.4018/ijssci.2017010102.

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Hardware virtualization has enabled large scale computational service delivery models with high cost leverage and improved resource utilization on cloud computing platforms. This has completely changed the landscape of computing in the last decade. It has also enabled large–scale data analytics through distributed high performance computing. Due to the infrastructure complexity, end–users and administrators of cloud platforms can rarely obtain a full picture of the state of cloud computing systems and data centers. Recent monitoring tools enable users to obtain large amounts of data with respect to many utilization parameters of cloud platforms. However, they fail to get the maximal overall insight into the resource utilization dynamics of cloud platforms. Furthermore, existing tools make it difficult to observe large-scale patterns, making it difficult to learn from the past behavior of cloud system dynamics. In this work, the authors describe a perceptual-based interactive visualization platform that gives users and administrators a cognitive view of cloud computing system dynamics.

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Soroko, N. V., and M. A. Shynenko. "Monitoring electronic educational and scientific resources using Google Analytics." CTE Workshop Proceedings 1 (March 21, 2013): 95–96. http://dx.doi.org/10.55056/cte.144.

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Currently, many scientific resources are created electronically. Such resources include: documents; electronic publications; electronic catalogs; pictures and images in different formats; audio, video, animation; digital maps and cartographic materials; computer programs, etc. It becomes important to analyze their relevance and the need for the development of science and education. Therefore, the problem of monitoring these resources arises.Google Analytics is a product of Google, which is part of the services called "cloud computing" (Cloud Computing).Cloud Computing is a data processing technology in which a software tool is provided to the user as an Internet service. For example, Google Analytics is a free service offered to create detailed statistics of website visitors.

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Mohan, P. Madana, and B. Murali Manohar. "Challenges in Big Data Analytics and Cloud Computing." International Journal of Business and Management Research 9, no. 2 (2021): 156–61. http://dx.doi.org/10.37391/ijbmr.090205.

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The advancement of technology has brought up so many elements of ease for human beings that now humans cannot afford to think of a life without all these. But all is not about ease and comfort. Along with all these elements, there are many modern complexities as well as challenges too. Big data analytics and cloud computing are among the elements in which we face many challenges. This document discusses some of the challenges in big data analytics and cloud computing.

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Journal articles on the topic 'Analytics Computing'

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