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Journal articles on the topic 'Data Analytics Infrastructure'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Mathias Kalema, Billy, and Motau Mokgadi. "Developing countries organizations’ readiness for Big Data analytics." Problems and Perspectives in Management 15, no. 1 (2017): 260–70. http://dx.doi.org/10.21511/ppm.15(1-1).2017.13.

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Regardless of the nature, size, or business sector, organizations are now collecting burgeoning various volumes of data in different formats. As much as voluminous data are necessary for organizations to draw good insights needed for making informed decisions, traditional architectures and existing infrastructures are limited in delivering fast analytical processing needed for these Big Data. For success organizations need to apply technologies and methods that could empower them to cost effectively analyze these Big Data. However, many organizations in developing countries are constrained wit

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Watson, Hugh J. "All About Analytics." International Journal of Business Intelligence Research 4, no. 1 (2013): 13–28. http://dx.doi.org/10.4018/jbir.2013010102.

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To understand and be successful with analytics, it is important to be precise in understanding what analytics means, the different targets or approaches that companies can take to using analytics, and the drivers that lead to the use of analytics. For companies that use advanced analytics, the keys to success include a clear business need; strong, committed sponsorship; a fact-based decision making culture; a strong data infrastructure; the right analytic tools; and strong analytical personnel in an appropriate organizational structure. These are the same factors for success for business intel

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Bansal, Pratik. "Smart Transportation Infrastructure: Leveraging IoT and Data Analytics for Improved Mobility." International Journal of Science and Research (IJSR) 9, no. 1 (2020): 1937–838. http://dx.doi.org/10.21275/sr24608140619.

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Smilowitz, Daniel, and Ashish Tiwari. "Supporting Grid Infrastructure Through Data‐Driven Analytics." Climate and Energy 39, no. 12 (2023): 1–8. http://dx.doi.org/10.1002/gas.22352.

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Guerrero-Prado, Jenniffer S., Wilfredo Alfonso-Morales, and Eduardo F. Caicedo-Bravo. "A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data." Sensors 21, no. 16 (2021): 5650. http://dx.doi.org/10.3390/s21165650.

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The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects. First, the architectural view places AMI within the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the transformation of raw d

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Ilmudeen, Aboobucker. "Big data analytics capability and organizational performance measures: The mediating role of business intelligence infrastructure." Business Information Review 38, no. 4 (2021): 183–92. http://dx.doi.org/10.1177/02663821211055321.

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The growing importance of big data has headed enterprises to advance their big data analytics capability to strengthen their firm performance. This study tests how big data capability impact on business intelligence infrastructure to achieve firm performance measures such as operational performance and marketing performance. This study is based on the recent literature on the knowledge-based view, big data capability, IT capability, and business intelligence. The primary survey of 272 responses from Chinese firms’ IT managers and big data analysts are used to uncover the relationship in the pr

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Hema Madhavi Kommula. "Optimizing financial services through big data analytics." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 2883–93. https://doi.org/10.30574/wjarr.2025.26.1.1355.

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The financial services industry has undergone a profound transformation through the strategic implementation of big data analytics, creating unprecedented opportunities for innovation and competitive differentiation. This comprehensive article examines the technological infrastructure supporting analytics in financial institutions, including data integration systems, machine learning frameworks, real-time processing platforms, cloud infrastructure, and natural language processing applications. The article explores five critical domains where analytics has demonstrated significant impact: custo

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Ushakov, Denis, Egor Dudukalov, Ekaterina Mironenko, and Khodor Shatila. "Big data analytics in smart cities’ transportation infrastructure modernization." Transportation Research Procedia 63 (2022): 2385–91. http://dx.doi.org/10.1016/j.trpro.2022.06.274.

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Lee, George, Jimmy Lin, Chuang Liu, Andrew Lorek, and Dmitriy Ryaboy. "The unified logging infrastructure for data analytics at Twitter." Proceedings of the VLDB Endowment 5, no. 12 (2012): 1771–80. http://dx.doi.org/10.14778/2367502.2367516.

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Barrile, Vincenzo, Stefano Bonfa, and Giuliana Bilotta. "Big Data Analytics for a Smart Green Infrastructure Strategy." IOP Conference Series: Materials Science and Engineering 225 (August 2017): 012195. http://dx.doi.org/10.1088/1757-899x/225/1/012195.

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Faiyaad, Chisis Mohammed, and Abayomi Bin Hakim Sadiki. "How healthcare industry in Arabs can use data science for sustainable healthcare practices." Business & IT XII, no. 1 (2022): 184–92. http://dx.doi.org/10.14311/bit.2022.01.22.

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To date, the healthcare business hasn't fully understood the prospective bene?ts to be acquired from big data analytics. Although the continuously growing body of academic investigation on large data analytics is mainly technology-oriented, a clear understanding of the strategic implications of big data is urgently needed. To handle the absence, this particular analysis examines the historical development, architectural style, and portion functionalities of big data analytics. From content evaluation of twenty six BDA implementation instances in healthcare, we could determine five big data ana

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Ciordas-Hertel, George-Petru, Jan Schneider, Stefaan Ternier, and Hendrik Drachsler. "Adopting Trust in Learning Analytics Infrastructure: A Structured Literature Review." JUCS - Journal of Universal Computer Science 25, no. (13) (2019): 1668–86. https://doi.org/10.3217/jucs-025-13-1668.

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One key factor for the successful outcome of a Learning Analytics (LA) infrastructure is the ability to decide which software architecture concept is necessary. Big Data can be used to face the challenges LA holds. Additional challenges on privacy rights are introduced to the Europeans by the General Data Protection Regulation (GDPR). Beyond that, the challenge of how to gain the trust of the users remains. We found diverse architectural concepts in the domain of LA. Selecting an appropriate solution is not straightforward. Therefore, we conducted a structured literature review to assess the s

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Famurewa, Stephen Mayowa, Liangwei Zhang, and Matthias Asplund. "Maintenance analytics for railway infrastructure decision support." Journal of Quality in Maintenance Engineering 23, no. 3 (2017): 310–25. http://dx.doi.org/10.1108/jqme-11-2016-0059.

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Purpose The purpose of this paper is to present a framework for maintenance analytics that is useful for the assessment of rail condition and for maintenance decision support. The framework covers three essential maintenance aspects: diagnostic, prediction and prescription. The paper also presents principal component analysis (PCA) and local outlier factor methods for detecting anomalous rail wear occurrences using field measurement data. Design/methodology/approach The approach used in this paper includes a review of the concept of analytics and appropriate adaptation to railway infrastructur

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Gaftandzhieva, Silvia, Rositsa Doneva, and Milen Bliznakov. "Monitoring of Student Enrolment Campaign through Data Analytics Tools." Mathematics and Informatics LXV, no. 5 (2022): 435–49. http://dx.doi.org/10.53656/math2022-5-5-mon.

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The market for new students is highly competitive. For this reason, higher education institutions (HEIs) can no longer rely on traditional strategies to hit enrolment goals. HEIs leadership must leverage new approaches, tools and skills to optimize the enrolment process, monitor the student enrolment campaign and improve marketing strategies to attract suitable students for future campaigns. This paper proposes a solution that facilitates and optimizes these processes. It introduces a model for monitoring student enrolment campaigns and a prototype of a correspondent software tool StEnrAnalyst

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Stamper, John, Steven Moore, Carolyn Rose, Philip Pavlik, and Kenneth Koedinger. "LearnSphere: A Learning Data and Analytics Cyberinfrastructure." Journal of Educational Data Mining (JEDM) 16, no. 1 (2024): 141–63. https://doi.org/10.5281/zenodo.11109638.

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LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation ineducation. It supports learning researchers in addressing a broad range of issues including cognitive, social,and motivational factors in learning, educational content analysis, and educational technology innovation.LearnSphere integrates previously separate educational data and analytic resources developed byparticipating institutions. The web-based workflow authoring tool, Tigris, allows technical users tocontribute sophisticated analytic methods, and learning researchers can adapt and

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R K, Monika, and Ravikumar K. "PROTECTING VIRTUALIZED INFRASTRUCTURES IN CLOUD COMPUTING BASED ON BIG DATA SECURITY ANALYTICS." ICTACT Journal on Soft Computing 11, no. 2 (2021): 2306–15. https://doi.org/10.21917/ijsc.2021.0330.

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Virtualized infrastructure in cloud computing has become an attractive target for cyber attackers to launch advanced attacks. This paper proposes a novel big data based security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs as well as user application logs collected periodically from the guest virtual machines (VMs) are stored in the Hadoop Distributed File System (HDFS). Then, extraction of attack features is performed through graph-based event correlation and Map Reduce parser based identification of potential attack paths. Next, determination

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Haggag, May, Ahmed Yorsi, Wael El-Dakhakhni, and Elkafi Hassini. "Infrastructure performance prediction under Climate-Induced Disasters using data analytics." International Journal of Disaster Risk Reduction 56 (April 2021): 102121. http://dx.doi.org/10.1016/j.ijdrr.2021.102121.

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Vats, Satvik, and B. B. Sagar. "An independent time optimized hybrid infrastructure for big data analytics." Modern Physics Letters B 34, no. 28 (2020): 2050311. http://dx.doi.org/10.1142/s021798492050311x.

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In Big data domain, platform dependency can alter the behavior of the business. It is because of the different kinds (Structured, Semi-structured and Unstructured) and characteristics of the data. By the traditional infrastructure, different kinds of data cannot be processed simultaneously due to their platform dependency for a particular task. Therefore, the responsibility of selecting suitable tools lies with the user. The variety of data generated by different sources requires the selection of suitable tools without human intervention. Further, these tools also face the limitation of recour

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Reisi, Marzieh, Soheil Sabri, Muyiwa Agunbiade, et al. "Transport sustainability indicators for an enhanced urban analytics data infrastructure." Sustainable Cities and Society 59 (August 2020): 102095. http://dx.doi.org/10.1016/j.scs.2020.102095.

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Frank, James E., and Mary Kay Falconer. "The measurement of infrastructure capacity: Theory, data structures, and analytics." Computers, Environment and Urban Systems 14, no. 4 (1990): 283–97. http://dx.doi.org/10.1016/0198-9715(90)90003-c.

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Kim, Joohyun, Sharath Maddineni, and Shantenu Jha. "Advancing next-generation sequencing data analytics with scalable distributed infrastructure." Concurrency and Computation: Practice and Experience 26, no. 4 (2013): 894–906. http://dx.doi.org/10.1002/cpe.3013.

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Navya Krishna Alapati. "Real-time data analytics and processing for adaptive load balancing in cloud infrastructures." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 538–46. https://doi.org/10.30574/wjaets.2025.14.3.0179.

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Dynamic load balancing is a key challenge in AI-enabled cloud infrastructures with volatile resource demand. This results in resource utilization drifting away from balance and creating performance loss, so the infrastructure starts to operate inefficiently. In this paper, we introduce a principled approach based on reinforcement learning and algorithmic optimization to dynamically allocate the load across the infrastructure. Our approach is based on reinforcement learning, providing instructions on what the ideal actions for load balancing in an ever-changing environment are. It takes advanta

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Dr.Shahnaz, Fatima. "RICH ANALYTICS WITH HADOOP TECHNOLOGY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 5 (2018): 561–63. https://doi.org/10.5281/zenodo.1252936.

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The period of ‘big data’ represents new challenges to businesses industry. Incoming data volumes are exploding in variety, speed, volume and complexity. It is not defined anywhere as such that how much volume of data will be considered as “big” data, but handling of such data requires lot of new tools and techniques to process it. To harness the power of Big data one needs an infrastructure and technologies which can deal with huge volume and variety of data as well as can draw inferences from it. There are various technologies for “Big Data Analysis” given

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Abhishek, Sharma, and Kumar Yogi Ayush. "The Role of IOT and Big Data Analytics in Driving Digital Transformation." Career Point International Journal of Research (CPIJR) 4, no. 2 (2024): 16–25. https://doi.org/10.5281/zenodo.11215459.

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This study aims to demonstrate the far-reaching impact of IoT and big data analytics in driving digital transformation across industries. Based on the interaction between  IoT devices and the big data they produce, it addresses the challenge of managing big data and extracting useful information from it. The focus includes data security, privacy, scalability, and optimizing data processing mechanisms. The main aim is to propose a strategy for using advanced analytical tools to extract meaningful patterns from data streams, thus facilitating decision-making and contributing to the ongoing

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Ajayi, Anuoluwapo, Lukumon Oyedele, Juan Manuel Davila Delgado, et al. "Big data platform for health and safety accident prediction." World Journal of Science, Technology and Sustainable Development 16, no. 1 (2019): 2–21. http://dx.doi.org/10.1108/wjstsd-05-2018-0042.

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Purpose The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy. Design/methodology/approach The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various t

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Nneka Adaobi Ochuba, Favour Oluwadamilare Usman, Enyinaya Stefano Okafor, Olatunji Akinrinola, and Olukunle Oladipupo Amoo. "PREDICTIVE ANALYTICS IN THE MAINTENANCE AND RELIABILITY OF SATELLITE TELECOMMUNICATIONS INFRASTRUCTURE: A CONCEPTUAL REVIEW OF STRATEGIES AND TECHNOLOGICAL ADVANCEMENTS." Engineering Science & Technology Journal 5, no. 3 (2024): 704–15. http://dx.doi.org/10.51594/estj.v5i3.866.

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Predictive analytics is transforming the maintenance and reliability of satellite telecommunications infrastructure, offering proactive solutions to prevent downtime and enhance operational efficiency. This conceptual review explores key strategies and technological advancements driving the adoption of predictive analytics in this field. The integration of IoT devices and sensors enables real-time monitoring, providing valuable data on equipment performance and environmental conditions. Advanced algorithms, such as AI and ML, analyze this data to predict equipment failures and optimize mainten

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Vinodhini Chandrasekaran. "AI-Powered Retail Analytics: Leveraging Spatial Data for Enhanced Store Performance." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 510–17. https://doi.org/10.32628/cseit25111247.

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This comprehensive technical article examines the transformation of retail operations through AI-driven spatial analytics and advanced tracking technologies. It explores the integration of customer behavior data with spatial analytics, providing retailers with unprecedented insights into shopping patterns and customer journeys. The article investigates various aspects of retail analytics implementation, including data collection infrastructure, journey analysis frameworks, and transaction integration systems. It demonstrates how retailers can leverage these technologies to optimize store layou

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Bhardwaj, Akashdeep, and Keshav Kaushik. "Predictive Analytics-Based Cybersecurity Framework for Cloud Infrastructure." International Journal of Cloud Applications and Computing 12, no. 1 (2022): 1–20. http://dx.doi.org/10.4018/ijcac.297106.

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The most valuable asset for any organization and individual is data and the information it holds. This is the main reason for Information Security to be the top concern in boardrooms and executive meetings. Security failures and data breaches now can impact an organization or a country's budget economy. To reduce Cybersecurity risks and improve data protection, there is an urgent need to implement a standard Framework for Cybersecurity. This framework utilizes AI and ML by including Policies, Guidelines, Standards and Practices, and data sources from Cloud Infrastructure systems like networks,

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Nwadiokwu, Obiajuru Triumph. "AI Augmented Digital Twins for IoT-Enabled Smart Infrastructure: Big Data Analytics for Real-time Optimization." International Journal of Research Publication and Reviews 6, no. 2 (2025): 1871–86. https://doi.org/10.55248/gengpi.6.0225.0901.

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Shanmukha Eetis. "Big Data Analytics for Smart Cities: Analysing Large Datasets to Optimize Urban Infrastructure and Services." Journal of Electrical Systems 20, no. 10s (2024): 3382–90. http://dx.doi.org/10.52783/jes.5802.

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In the modern era of urbanization, the concept of smart cities has emerged as a transformative approach to enhance urban living through the integration of technology and data-driven strategies. Big Data Analytics plays a pivotal role in this transformation, providing the capability to analyze vast and complex datasets generated by various urban activities and infrastructures. This paper delves into the application of Big Data Analytics in optimizing urban infrastructure and services, focusing on transportation, energy management, waste disposal, and public safety. By leveraging real-time data

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Chen, Xiaofeng, and Keng Siau. "Business Analytics/Business Intelligence and IT Infrastructure." Journal of Organizational and End User Computing 32, no. 4 (2020): 138–61. http://dx.doi.org/10.4018/joeuc.2020100107.

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This is an empirical research investigating the impact of business analytics (BA) and business intelligence (BI) use, IT infrastructure flexibility, and their interactions on organizational agility. Synthesizing the systems theory and awareness-motivation-capability framework, the authors propose that BA-Use, IT infrastructure flexibility, and their interactions significantly influence organizational agility. The results show the significant association of BA use and IT infrastructure flexibility with organizational agility. The results also suggest that BA use may demand corporations to build

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Elena, Vasilieva, Rajat Singh, Rajeev Sobti, Kshama Sharma, Rajat Sharma, and P. Surekha. "Leveraging Big Data Analytics for Urban Planning: A Study Using the Big Data Analytics Efficiency Test." BIO Web of Conferences 86 (2024): 01082. http://dx.doi.org/10.1051/bioconf/20248601082.

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Data from a variety of sample cities was evaluated as part of a research looking into the integration of big data analytics into urban planning. The goals were to evaluate the impact of data analytics infrastructure, data volume and processing time, urban development initiatives, and data analytics efficiency. The results showed significant differences in data analytics resources across cities, indicating different levels of investment and preparedness for data-driven decision making. It was clear that cities could handle large amounts of data efficiently thanks to their strong data processing

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

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

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A., Jangili*1 S. Ramakrishnan2 S. Seth3. "Harnessing Data Analytics for Improving Management Information Systems (MIS) in Healthcare." International Journal of Pharmaceutical Sciences 3, no. 1 (2025): 1787–95. https://doi.org/10.5281/zenodo.14709903.

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Data-driven strategies are being used more and more by healthcare systems to boost patient outcomes, increase operational effectiveness, and optimize resource management. Finding inefficiencies, forecasting patient needs, and assisting with evidence-based decision-making have all been made possible by data analytics. However, small and medium-sized healthcare facilities face barriers such as limited financial resources, inadequate technical infrastructure, and insufficient expertise that prevent them from leveraging these advancements. The above problems require a proposal to design light clou

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Yang, Yifan, S. Thomas Ng, Frank J. Xu, Martin Skitmore, and Shenghua Zhou. "Towards Resilient Civil Infrastructure Asset Management: An Information Elicitation and Analytical Framework." Sustainability 11, no. 16 (2019): 4439. http://dx.doi.org/10.3390/su11164439.

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It is rather difficult for the stakeholders to understand and implement the resilience concept and principles in the infrastructure asset management paradigm, as it demands quality data, holistic information integration and competent data analytics capabilities to identify infrastructure vulnerabilities, evaluate and predict infrastructure adaptabilities to different hazards, as well as to make damage restoration and resilience improvement strategies and plans. To meet the stakeholder’s urgent needs, this paper proposes an information elicitation and analytical framework for resilient infrastr

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Gates, John Doe, Yulianti Yulianti, and Greian April Pangilinan. "Big Data Analytics for Predictive Insights in Healthcare." International Transactions on Artificial Intelligence (ITALIC) 3, no. 1 (2024): 54–63. http://dx.doi.org/10.33050/italic.v3i1.622.

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This study leverages the transformative power of big data analytics to enhance healthcare outcomes by integrating diverse data sources like electronic health records, medical imaging, and genomic data to refine predictive models that forecast disease progression and personalize treatment strategies. Employing rigorous data management and machine learning, our findings demonstrate effective risk factor identification and resource optimization, significantly reducing hospital readmissions and improving chronic disease management as evidenced by a case study at City Hospital. Despite challenges r

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Kim, Byeong Jo, and Maria Tomprou. "The Effect of Healthcare Data Analytics Training on Knowledge Management: A Quasi-Experimental Field Study." Journal of Open Innovation: Technology, Market, and Complexity 7, no. 1 (2021): 60. http://dx.doi.org/10.3390/joitmc7010060.

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This study aims to provide practice-oriented evidence regarding the implementation of healthcare data analytics and its impact on the use of new data analytics tools and relevant analytical skills improvement. A quasi-experimental pre-test/post-test controlled study was conducted in a large medical system in the eastern United States. Healthcare data analytics training program participants (N = 21) and a comparison group comprising trainee-identified peers completing comparable work (N = 27) were compared at the start of training and one year later. Results showed that both trainees and peers

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F. G. Assis, Luiz Fernando, Karine Reis Ferreira, Lubia Vinhas, et al. "TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping." ISPRS International Journal of Geo-Information 8, no. 11 (2019): 513. http://dx.doi.org/10.3390/ijgi8110513.

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The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil,

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Sobolevsky, S., S. McIntosh, and P. C. K. Hung. "Editorial Preface: Special Issue on Big Data Analytics, Infrastructure, and Applications." IEEE Transactions on Services Computing 9, no. 1 (2016): 2–3. http://dx.doi.org/10.1109/tsc.2015.2509738.

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Ji, Chuanyi, Yun Wei, and H. Vincent Poor. "Resilience of Energy Infrastructure and Services: Modeling, Data Analytics, and Metrics." Proceedings of the IEEE 105, no. 7 (2017): 1354–66. http://dx.doi.org/10.1109/jproc.2017.2698262.

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Jeppesen, Jacob Høxbroe, Emad Ebeid, Rune Hylsberg Jacobsen, and Thomas Skjødeberg Toftegaard. "Open geospatial infrastructure for data management and analytics in interdisciplinary research." Computers and Electronics in Agriculture 145 (February 2018): 130–41. http://dx.doi.org/10.1016/j.compag.2017.12.026.

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Longley, Paul A. "Smart Data infrastructure for urban analytics, city science, planning and design." Environment and Planning B: Urban Analytics and City Science 51, no. 5 (2024): 1041–44. http://dx.doi.org/10.1177/23998083241246327.

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Kuzmin, M. A. "Comparative Analysis of Data Analytics Approaches in the Context of Business Digital Transformation." Scientific notes of the Russian academy of entrepreneurship 23, no. 3 (2024): 19–28. http://dx.doi.org/10.24182/2073-6258-2024-23-3-19-28.

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The article is dedicated to exploring data analytics approaches within the context of business digital transformation. The role of data in enhancing the efficiency of enterprises is highlighted, as data facilitates informed managerial decision–making and strengthens competitive advantages. It is emphasized that the effective use of data requires not only advanced infrastructure and personnel competencies but also a systematic approach that integrates various methods of data analysis and justifies the transition to specific business analytics strategies. A comparative analysis of three primary

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

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

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Oursatyev, Oleksii A. "Data Research in Industrial Data Mining Projects in the Big Data Generation Era." Control Systems and Computers, no. 3 (303) (2023): 33–53. http://dx.doi.org/10.15407/csc.2023.03.033.

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Introduction. The review material is based mainly on business intelligence (BI) solutions designed for tasks with corporate data. But all the main aspects of working with data discussed in the work are also used on data processing platforms (Data Science Platform). Many BI vendors have expanded the capabilities of their systems to perform more advanced analytics, including Data Science. They added the phrase “Data Science” to their marketing research, and the term “advanced analytics” lost some popularity in relation to corporate data. The Data Science Platform provides a comprehensive set of

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Ansari, Abdul Aziz, M. Abdul Rehman, Ahmad Waqas, and Shafaq Siddiqui. "Spatial Data Analysis: Recommendations for Educational Infrastructure in Sindh." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 96. http://dx.doi.org/10.30537/sjcms.v1i1.12.

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Analysing the Education infrastructure has become a crucial activity in imparting quality teaching and resources to students. Facilitations required in improving current education status and future schools is an important analytical component. This is best achieved through a Geographical Information System (GIS) analysis of the spatial distribution of schools. In this work, we will execute GIS Analytics on the rural and urban school distributions in Sindh, Pakistan. Using a reliable dataset collected from an international survey team, GIS analysis is done with respect to: 1) school locations,

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Gonçalves, Frederico, Liselene de Abreu Borges, and Rodrigo Batista. "Electric Vehicle Charging Data Analytics of Corporate Fleets." World Electric Vehicle Journal 13, no. 12 (2022): 237. http://dx.doi.org/10.3390/wevj13120237.

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The advances in electric mobility, motivated by current sustainability issues, have led public and private organizations to invest in the electrification of their corporate fleets. To succeed in this transition, companies must mitigate the impacts of electrification on their fleet operation, in particular the ones on vehicle recharging. The increase in energy demand caused by electrification may require changes in the company electrical infrastructure, the installation of charging stations, and the proper planning of the recharging schedule, considering the particularities of each fleet and op

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Yue, Peng, Kaixuan Wang, Hanwen Xu, Jianya Gong, and Longgang Xiang. "From Geospatial Data Cube to AI Cube: the Open Geospatial Engine (OGE) Approach." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4-2024 (October 18, 2024): 441–46. http://dx.doi.org/10.5194/isprs-annals-x-4-2024-441-2024.

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Abstract. The Earth Observation (EO) analytics are moving from local systems to online cloud computing platforms such as Google Earth Engine (GEE) and Open Geospatial Engine (OGE). A typical approach in existing efforts is to leverage geospatial data cubes with cloud computing to support large-scale big EO data analytics in Digital Earth systems. While online analytical processing (OLAP) can be enabled using the cube approach, it is still not clear how geospatial artificial intelligence (GeoAI) can be incorporated in data cubes to benefit the cube infrastructure. Such an investigation can cons

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Guerrero-Prado, Jenniffer Sidney, Wilfredo Alfonso-Morales, Eduardo Caicedo-Bravo, Benjamín Zayas-Pérez, and Alfredo Espinosa-Reza. "The Power of Big Data and Data Analytics for AMI Data: A Case Study." Sensors 20, no. 11 (2020): 3289. http://dx.doi.org/10.3390/s20113289.

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In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI). Such technology gets user consumption characteristics at levels of detail that were previously not possible. In this context, the terms big data and data analytics become relevant, which are tools that allow using large volumes of in

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Dr. N. Siva Surendra, Ms. V. Sailaja, Mr. Ch. Ravindranath, Mr. S. Suresh Babu, Ms. Ch. Sujatha, and Mrs. A. Siva Naga Lakshmi. "A Study on Steps in Building Data Infrastructure for Data-Driven HR Practices in Modern Organizations (HR Analytics Perspective)." Economic Sciences 20, no. 1 (2024): 164–70. http://dx.doi.org/10.69889/pw8csb49.

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HR analytics is the process of gathering and analyzing HR data to yield useful insights, enhance decision-making, and energize the workplace with data accuracy. Working dimensions are changing every minute in the world with advanced insights into the work environment. As a part of the progression advanced technologies are adopted in organizations. New landscapes and horizons are made a pool of data as employees work 24/7, work from home, destination works, and various alternatives give rise to the use of the data. Now the data is new blood for the old organizations. So, in this parlance, the u

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