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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.<strong>Material and Methods:</strong> A qualitative research approach was followed as well as an interpretivism paradigm and a single case study research strategy. Semi-structured interviews were employed for data collection from eleven professionals in the Information Technology division of a South African bank.<strong>Results: In total,</strong> 35 technology factors required for adopting cloud-based big data analytics were identified in this study and furthermore categorized into; internal cloud-based big data analytics criteria, cloud-based big data analytics capabilities or skills, cloud-based big data analytics data integrity levels, data security and readiness for adopting cloud-based big data analytics and cloud-based big data analytics external criteria.<strong>Conclusions:</strong> The results of this study could imply that the adoption of cloud-based big data analytics in the banking sector takes into consideration an outsourcing model or setting. In this structure, technology factors are not only specific to the bank concerned. The banking sector has its own technology requirements that banks are expected to adhere to or take into consideration, while some technology factors could only be addressed by the cloud-based big data analytics service providers. The identified factors could be used in the conceptualization of a cloud-based big data analytics framework in future research.
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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 intelligence, big data analytics, advanced analytics, and business intelligence. The innovative data analysis process of cloud-based AI and big data analytics is transforming business processes too. The findings are expected to generate new knowledge about the role of contemporary AI and big data analytical tools in business intelligence and to bridge the gap between AI, business intelligence, and big data analytics by investigating the effect of AI and big data analytics on business intelligence environments. Furthermore, it holds the potential to motivate and encourage further studies in utilizing new AI and big data analytical techniques in the field of business decision-making.</em> <em>Real-time decision-making has become a significant aspect of business operations in the era of digitization and the technological evolution of contemporary artificial intelligence, deep learning, and machine learning. The theoretical and industry-oriented analysis of artificial intelligence, big data analytics, and personal learning accurately in the context of cloud computing is lacking. The purpose of this essay is to understand the effect of cloud-based AI and big data analytics on business decision-making. The findings of the essay may yield an innovative understanding of groundbreaking AI and personal data analytical techniques in the field of business intelligence and decision-making under complex situations. </em>
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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 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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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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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 big data and mobile cloud technologies.
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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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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 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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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 attributes. A specified examination of cloud-primarily based Big Data frameworks together with Apache Hadoop and Apache Spark, together with managed services from principal cloud vendors, gives insights into the various alternatives to be had. The position of cloud-local garage answers, data control techniques, and strategies for scalability and overall performance optimization are dissected. Security and privacy issues in cloud-primarily based Big Data analytics are scrutinized, encompassing encryption mechanisms and compliance frameworks. The evaluate contemplates the challenges inherent inside the area and envisions futureinstructions, which includes hybrid cloud architectures and edge computing integration. Industry case studies illustrate practical applications across finance, healthcare, and e-commerce. The end synthesizes key findings, emphasizing the transformative effect of cloud-based totally Big Data analytics on selection-making and innovation. This complete survey serves as a precious resource for researchers, practitioners, and decision-makers navigating the dynamic intersection of Big Data analytics and cloud computing.
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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 changing situations. To fix the urgent problem of weak security in cloud sys- tems, this research introduces an AI-powered big data analytics system. The aim is to use artificial intelligence and big data technologies to improve spot- ting threats, marking unusual incidents and reducing risks as they happen. Machine learning and deep learning are used within the system which makes use of distributed processing platforms such as Apache Spark, Hadoop and Kafka. Together, these pieces ensure that a lot of log data and telemetry from hybrid and multi-cloud setups are ingested, worked on and analyzed quickly and efficiently. The proposed solution uses Isolation Forests, Ran- dom Forests, Autoencoders and LSTM networks to spot abnormal activity and risks. They can recognize unusual patterns in network activity, website logs and API usage to find out about possible attacks. It also makes use of natural language processing to study unstructured log data for threats and compares these to the ones listed in external threat intelligence. The archi- tecture is built with a layer using Kafka and Logstash to get data ingested, another using Spark and HDFS for processing and a third for real-time threat analysis and prediction with AI. Information about threats is presented vi- sually in dashboards with the help of Grafana and Kibana, so analysts can easily respond to any threats. Risks are scored with a mechanism that focuses on the worst incidents and those expected to have the biggest impact. Bench- mark datasets such as CICIDS 2017 and UNSW-NB15 are used, along with anonymized real-world activity logs from the cloud, to assess the suggested solution’s robustness. The data suggests that using this technology is more effective and faster than using traditional security approaches. This study has resulted in an AI-based security framework that can handle large enter- prise loads, adaptive security models and affordable implementation paths for the cloud. Thanks to this work, cloud security can now focus on ad- vancing to automating early detection, providing continuous monitoring and implementing automatic steps when needed. Ultimately, the use of AI and big data analytics changes how cloud security functions. This research en- ables systems to detect threats and rate risks in real time, helping to improve the security of today’s cloud networks.
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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, improve customer experiences, and foster competitive advantages. The research delves into key cloud architectures, data management techniques, and analytics tools that underpin big data solutions, while addressing challenges such as data security, privacy, and compliance. Furthermore, the study reviews industry use cases across various sectors, illustrating how big data in cloud environments enhances productivity and innovation. The findings underscore the pivotal role of cloud computing in unlocking the full potential of big data, offering a roadmap for businesses aiming to capitalize on data-driven strategies.
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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 service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.
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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 challenges, including data security, integration complexities, and performance bottlenecks, while proposing solutions such as encryption, real-time analytics frameworks, and hybrid cloud models. Furthermore, it discusses industry applications across healthcare, finance, IoT, and business intelligence. The findings demonstrate that cloud-based Big Data solutions enhance operational efficiency, decision making, and scalability, paving the way for future advancements in AI-driven analytics, edge computing, and cross-cloud integrations.
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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. Additionally, it opens a new horizon for researchers to develop the solution, based on the challenges and open research issues.
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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 of the information. So the paper proposes the big data analytics for the fog assisted health care application to effectively handle the health information’s diagnosed for the aged persons. The proposed model is simulated using the IFogSim toolkit to examine the performance fogassisted smart healthcare application.
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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, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications.
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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 analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.
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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 pros and cons. The story goes into great detail about how Amazon Redshift helps businesses use the power of their data on a large scale, which leads to new ideas and a competitive edge in today's data-driven business world.
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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 more digitalized. This study presents Big Data and highlights its key concepts and state-of-the-art implementation as well as research challenges and suggests research directions for future. IT log analytics, Fraud detection pattern, social media pattern and modeling and management patterns are some of opportunities. Hadoop is a cloud based and open source solution for Big Data Analytics which has been written by java. Hadoop solution is currently still immature. In this paper, three topics are suggested for research direction: Security issues in Big Data, context-aware information retrieval, and integrating ontology with Big Data.</p>
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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 big data together with basic Map Reduce structure. Then it presents existing approaches for big data analytics including general architecture. The second part establishes the relation between mobile cloud and big data and provides geo-dispersed big data application, including big data in mobile cloud sensing. Some open research questions that need to be investigate conclude this work.
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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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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 machine learning technology, combined with analytics, supports banks to research a great deal on customer behavior and preferences, allowing banks to continuously learn and fine tune analytical models to optimize products and services and minimize the cost of offering products across different channels. Cloud-based analytics platforms provide flexibility and elasticity for banks to work at high speed with large data workloads and to gain business value more quickly. In this paper, the major business analytics components - descriptive analytics, predictive analytics, and prescriptive analytics are addressed and their applications in various functions of banks for optimum decision-making as well as for activities such as fraud detection, application screening, custom acquisition and retention, awareness of customer purchasing habits, effective cross selling of different banking products and services, payment collection mechanism, better cash/liquidity planning, marketing optimization, consumer lifetime value, management of customer reviews, etc are analyzed. The effects of these analytics on the banking and financial industry sector's competitive and innovative capabilities are also discussed.
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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 process control business. It also presents various crucial issues such as data protection, adherence to legal requirements, and compatibility with other systems, giving solutions. In addition, the research compares the effectiveness of cloud-based solutions with on-premise ones and discuss other novelties, including edge computing and artificial intelligence as the tendencies potentially influencing process control. Consequently, the findings of this research can be helpful for both industry practitioners and researchers who aim to optimize process control and organization operation with the help of cloud-based big data analytics
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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 technology are their early childhood, many people were confused with its relevancy and applicability. Cloud Computing is Infrastructure based solution for managing data and computational framework. 2016 was a significantly more important year for this volumes data technology or Big Data eco system as large number of enterprises, and organizations are generating data, storing that data and worried about future aspect of that data. In 2017, corporate world take cognizance of their large volumes structured and unstructured data as these enterprises and organizations continuously generating large volumes data. The term big data doesn’t just refer to the massive amounts of data existing today, it also refers to the whole ecosystem of Storing or gathering data, Different types of data and analyzing that data. In traditional data ecosystem all leverages are with legacy system. Transforming or migration of these traditional ecosystems to the cloud is full of great challenges and benefits. Cloud computing is an agile and scalable resource access computation paradigm, provides heterogeneous platform seamlessly with infrastructure of internet, exclusively for the trapped and work on pre and post process of big data. Now the challenges are finding opportunity and challenges for managing, migrating and abstracting cloud based big data using cloud infrastructure for future eco system of Big Data Analysis. This paper is basically focused on this issue. We try to reevaluate the facts of existing Cloud Infrastructure as IaaS for tomorrow’s big data analytics.
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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 chain management (SCM). Even though big data technologies promise many benefits and advantages, the prospective applications of big data technologies in sustainable SCM are still not achieved to a full extent. This necessitates work on several segments like research, the design of new models, architectures, services, and tools for big data analytics. The goal of the paper is to introduce a methodology covering the whole Business Intelligence (BI) lifecycle and a unified model for advanced supply chain big data analytics (BDA). The model is multi-layered, cloud-based, and adaptive in terms of specific big data scenarios. It comprises business process modeling, data ingestion, storage, processing, machine learning, and end-user intelligence and visualization. It enables the creation of next-generation BDA systems that improve supply chain performance and enable sustainable SCM. The proposed supply chain BDA methodology and the model have been successfully applied in practice for the purpose of supplier quality management. The solution based on the real-world dataset and the illustrative supply chain case are presented and discussed. The results demonstrate the effectiveness and applicability of the big data model for intelligent and insight-driven decision making and sustainable supply chain management.
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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 proposed. This survey depicts the limitations of the existing approaches and the requirements for a novel and enhanced approach that ensures integrity of the data stored in cloud enabling better performance with reduced complexity.
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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 "Big Data Analytics: A Brief Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd15617.pdf
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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 trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and selfsufficient. The integrating big data with cloud computing technologies, businesses and education institutes can have a better direction to the future. The capability to store large amounts of data in different forms and process it all at very large speeds will result in data that can guide businesses and education institutes in developing fast. Nevertheless, there is a large concern regarding privacy and security issues when moving to the cloud which is the main causes as to why businesses and educational institutes will not move to the cloud. This paper introduces the characteristics, trends and challenges of big data. In addition to that, it investigates the benefits and the risks that may rise out of the integration between big data and cloud computing.
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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 unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.
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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 trailing with higher extraction of its linguistics. If the application is used worldwide, the data generated out of it is Big Data, which traditional systems cannot handle. We therefore propose a special framework for efficient data collection, storage and processing at Big Data scale on cloud platform.
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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 individual interactions the big data are continuously created or re-created by offline and online activities, the actual solution design by offering power of analytics have not been discussed at a greater extent over the past in academic outlets. In this paper, we introduce a combined requirement of developing cloud-based analytics system to handle, retrieve and manipulate the big data for improving decision making in supply chain management. The main emphasis in the study goes after outlining a conceptual analytics approach for meeting the decision support needs in a hypothetical supply chain industry problem-domain, specially focusing on decision support application for various individual chain managers.
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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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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 framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.
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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 predictive decisions. These machine learning models form the backbone for the generation and development of AI-assisted decisions. In many cases, such decisions are automated using systems such as chatbots and robots.Of special interest are issues such as omnichannel shopping behavior, resource allocation across channels, the effects of the mobile channel and mobile apps on shopper behavior, dynamic pricing, data privacy and security. Research on these issues reveals several interesting insights on which retailers can build. To fully leverage big data in today’s retailing environment, CRM strategies must be location specific, time specific and channel specific in addition to being customer specific.
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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 can be leverage this data in further predictions. Cloud computing has now turned out to be a big alternative while handling big data because cloud itself carry certain features which help in analyzing and accessing big data in proper manner.Before switching to Cloud based approaches it provides an ease of set up or testing and is economical.Thus there is a demand for cloud computing and machine learning techniques with Hadoop or Spark.Mainly we are focusing on various works that have been done in handling big data. Here the analysis of various algorithms that are used by various researches in handling big data as well as outcome that they obtained in overcoming the challenges in handling big data.
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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 used to automate governance tasks, predict compliance risks, and provide real-time auditing, while blockchain plays a critical role in ensuring data integrity and transparency across distributed cloud environments. Moreover, the research underscores the need for flexible and scalable governance models that can adapt to evolving regulations like GDPR and CCPA. Additionally, best practices such as multi-layered security approaches and strong collaboration with cloud service providers were identified as key strategies for enhancing governance frameworks. These insights contribute to the ongoing discourse on the modernization of data governance, highlighting the importance of dynamic, automated, and proactive approaches to managing data in cloud-based environments. This study provides a comprehensive understanding of current practices and technological innovations, offering actionable recommendations for organizations navigating the complexities of cloud-based data governance.
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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 challenges of big data and how the recent technologies can be used to address those issues, which are illustrated through real world case studies. The article also presents the lessons learned from these case studies.
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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, and shows how businesses can benefit from using AI in their data preparation process for cloud analytics. The use of diagrams, flowcharts, and pseudocode helps explain these concepts in a simplified yet technical manner. Keywords AI-driven data preparation, cloud-based analytics, data pipeline, automation, machine learning, data wrangling, ETL, data transformation, big data
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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 and conducting elasticity of IoT big data analytics applications on the cloud, similar work on fog computing is not many. This article firstly introduces AutoFog, a fog-computing framework, which provides holistic deployment and an elasticity solution for fog-based IoT big data analytics applications including a novel mechanism for elasticity provision. Secondly, the article also points out requirements that a framework of IoT big data analytics application on fog environment should support. Finally, through a realistic smart home use case, extensive experiments were conducted to validate typical aspects of our proposed framework.
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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 benefits of the cloud are obvious -- once you get there. Moving to the cloud requires planning, strategy, and the right tools for data migration. [1] By using Snowflake's advanced data warehousing tools, healthcare organizations can smoothly handle and analyze their complex and varied data. This helps them quickly uncover important insights and make better decisions. The shift to cloud technology and Snowflake has the potential to significantly enhance real-time analytics, personalized patient care, and evidence-based decision-making in healthcare. When healthcare organizations leverage big data in a cloud-based setting, they can discover valuable insights from their data, ultimately improving clinical outcomes, operational efficiency, and healthcare delivery. This study explores how the adoption of cloud and Snowflake in healthcare analytics can bring about transformative change and create new possibilities for leveraging data and generating insights in the healthcare sector.
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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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Frank, Ahairwe, and Atukunda Lucky. "Leveraging Big Data for Effective Educational Management." IDOSR JOURNAL OF ARTS AND MANAGEMENT 10, no. 1 (2025): 1–5. https://doi.org/10.59298/idosrjam/2025/101.15000.
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This paper examines the transformative role of big data in educational management, emphasizing its potential to enhance decision-making, streamline administrative operations, and improve student outcomes. With the proliferation of digital tools in education, big data provides actionable insights for personalized learning, resource optimization, and institutional performance. The study outlines the benefits, challenges, and limitations of implementing big data analytics in educational settings, including ethical considerations and infrastructural demands. Key technologies and tools such as predictive analytics, machine learning, and cloud computing are discussed, along with case studies highlighting best practices and lessons learned. This paper emphasizes the importance of evidence-based strategies in building innovative and effective educational systems. Keywords: Big Data, Educational Management, Predictive Analytics, Personalized Learning, Data-Driven Decision Making, Learning Analytics.