Journal articles: 'Analytics infrastructure'

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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 intelligence in general, but there are important nuances when implementing advanced analytics, such as with the data infrastructure, analytical tools, and personnel. Companies like Amazon.com, Overstock.com, Harrah’s Entertainment, and First American Corporation are exemplars that illustrate concepts and best practices.

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Zagorecki, Adam, Jozef Ristvej, and Krzysztof Klupa. "Analytics for Protecting Critical Infrastructure." Communications - Scientific letters of the University of Zilina 17, no. 1 (2015): 111–15. http://dx.doi.org/10.26552/com.c.2015.1.111-115.

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Mestre, Luís Rolo, Andreia Grossinho, Carlos Pedro Conde, Marta Ratão, and Pedro Chaves Ferreira. "Advanced analytics enhancing infrastructure availability." Transportation Research Procedia 72 (2023): 604–10. http://dx.doi.org/10.1016/j.trpro.2023.11.445.

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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 infrastructure maintenance. The diagnostics aspect of the proposed framework is demonstrated with a case study using historical rail profile data collected between 2007 and 2016 for nine sharp curves on the heavy haul line in Sweden. Findings The framework presented for maintenance analytics is suitable for extracting useful information from condition data as required for effective rail maintenance decision support. The findings of the case study include: combination of the two statistics from PCA model (T2 and Q) can help to identify systematic and random variations in rail wear pattern that are beyond normal: the visualisation approach is a better tool for anomaly detection as it categorises wear observations into normal, suspicious and anomalous observations. Practical implications A practical implication of this paper is that the framework and the diagnostic tool can be considered as an integral part of e-maintenance solution. It can be easily adapted as online or on-board maintenance analytic tool with data from automated vehicle-based measurement system. Originality/value This research adapts the concept of analytics to railway infrastructure maintenance for enhanced decision making. It proposes a graphical method for combining and visualising different outlier statistics as a reliable anomaly detection tool.

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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 with limited access to technology, finances, infrastructure and skilled manpower. Yet, for productive use of these technologies and methods needed for Big Data analytics, both the organizations and their workforce need to be prepared. The major objective for this study was to investigate developing countries organizations’ readiness for Big Data analytics. Data for the study were collected from a public sector in South Africa and analyzed quantitatively. Results indicated that scalability, ICT infrastructure, top management support, organization size, financial resources, culture, employees’ e-skills, organization’s customers’ and vendors are significant factors for organizations’ readiness for Big Data analytics. Likewise strategies, security and competitive pressure were found not to be significant. This study contributes to the scanty literature of Big Data analytics by providing empirical evidence of the factors that need attention when organizations are preparing for Big Data analytics.

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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 a more flexible IT infrastructure. However, the data does not reveal the proposed interaction between the two drivers of organizational agility.

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Omelianenko, Olena, and Andrii Basov. "Scientific and Methodological Aspects of Analytics of Modern Business Models of Infrastructural Solutions." Economics: time realities 2, no. 72 (2024): 39–47. http://dx.doi.org/10.15276/etr.02.2024.5.

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Modern infrastructure is a complex of systems, networks, objects and technologies that ensure the effective functioning of society and the economy in the conditions of modern challenges and technological progress. The creation and maintenance of such infrastructure is a critically important aspect for the development of the country, its competitiveness and ability to adapt to new conditions. The introduction of innovative methods, such as the business process approach, allows creating efficient and adaptive infrastructure systems. At the same time, business practices have not become sufficiently widespread in infrastructure enterprises, which is an important problem in the conditions of directing significant funds to restore damaged and create new infrastructure. The purpose of the research is to determine the features of the business model of infrastructural solutions and to develop proposals for evaluating its effectiveness. The study identifies the key elements of the infrastructure services business model. The main directions of transformation of business models in the infrastructural sphere are considered. In accordance with modern management standards, the key principles of the concept of continuous improvement of business processes in the field of service enterprises have been defined. DEA analysis was used for the comparative assessment of the effectiveness of the business model of business structures based on the results.

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Omelianenko, Olena, and Andrii Basov. "Scientific and Methodological Aspects of Analytics of Modern Business Models of Infrastructural Solutions." Economics: time realities 2, no. 72 (2024): 39–47. https://doi.org/10.5281/zenodo.11238189.

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Modern infrastructure is a complex of systems, networks, objects and technologies that ensure the effective functioning of society and the economy in the conditions of modern challenges and technological progress. The creation and maintenance of such infrastructure is a critically important aspect for the development of the country, its competitiveness and ability to adapt to new conditions. The introduction of innovative methods, such as the business process approach, allows creating efficient and adaptive infrastructure systems. At the same time, business practices have not become sufficiently widespread in infrastructure enterprises, which is an important problem in the conditions of directing significant funds to restore damaged and create new infrastructure. The purpose of the research is to determine the features of the business model of infrastructural solutions and to develop proposals for evaluating its effectiveness. The study identifies the key elements of the infrastructure services business model. The main directions of transformation of business models in the infrastructural sphere are considered. In accordance with modern management standards, the key principles of the concept of continuous improvement of business processes in the field of service enterprises have been defined. DEA analysis was used for the comparative assessment of the effectiveness of the business model of business structures based on the results.

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Butte, Atul J. "Challenges in bioinformatics: infrastructure, models and analytics." Trends in Biotechnology 19, no. 5 (2001): 159–60. http://dx.doi.org/10.1016/s0167-7799(01)01603-1.

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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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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 proposed model. The finding shows that the big data analytics capability significantly impacts on business intelligence infrastructure that in turn positively impact on operational performance and marketing performance. Further, the business intelligence infrastructure partially mediates between big data analytics capability and operational performance, and fully mediates between big data analytics capability and marketing performance. This research contributes to the information systems literature such as big data analytic capability, business intelligence, and firm performance measures, and thus offers grounds to extend more widespread studies in this field. This study adds to the literature on the theory and practical bases for big data capability and business intelligence infrastructure.

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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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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 infrastructure asset management. The framework is devised by leveraging the best practices and processes of integrated infrastructure asset management and resilience management in the literature, synergizing the common elements and critical concepts of the two paradigms, ingesting the state-of-the-art interconnected infrastructure systems resilience analytical approaches, and eliciting expert judgments to iteratively improve the derived framework. To facilitate the stakeholders in implementing the framework, two use case studies are given in this paper, depicting the detailed workflow for information integration and resilience analytics in infrastructure asset management. The derived framework is expected to provide an operational basis to the quantitative resilience management of civil infrastructure assets, which could also be used to enhance community resilience.

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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 maintenance schedules. These technologies improve the accuracy of predictive models, allowing companies to reduce downtime and improve overall infrastructure reliability. Challenges include data privacy and security concerns, as well as the integration of predictive analytics into existing maintenance processes. Companies must invest in specialized skills and expertise to implement predictive analytics successfully. Looking ahead, emerging technologies like real-time analytics and AI will continue to shape the future of predictive analytics in satellite telecommunications. Standardized practices, collaboration with industry partners, and a focus on data quality are essential for companies to harness the full potential of predictive analytics. In conclusion, predictive analytics is a game-changer for the maintenance and reliability of satellite telecommunications infrastructure. By adopting predictive analytics, companies can optimize maintenance processes, reduce downtime, and improve overall infrastructure reliability. Keywords: Predictive Analytics, Infrastructure, Telecommunications, Satellite, Reliability.

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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, servers, security systems, and end-user devices. Combining the data set gathered and risk governance information with Artificial Intelligence and Machine Learning. This research presents a framework that collects datasets, enriches and validates logs and datasets, then correlates them to analyze and predict the response to Cyber attack with high level of accuracy using ML model.

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Helles, Rasmus, and Mikkel Flyverbom. "Meshes of Surveillance, Prediction, and Infrastructure: On the Cultural and Commercial Consequences of Digital Platforms." Surveillance & Society 17, no. 1/2 (2019): 34–39. http://dx.doi.org/10.24908/ss.v17i1/2.13120.

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Digital platforms like Spotify, Netflix, and YouTube rely on mass data collection, algorithmic forms of prediction, and the development of closed digital systems. Seemingly technical and trivial, such operational and infrastructural features have both commercial and cultural consequences in need of attention. As with any other kinds of infrastructure, the surveillance practices and digital ecosystems that are now installed and solidified will have long-term effects and will be difficult to challenge. We suggest that the cultural and commercial ramifications of such datafied infrastructural developments can be unpacked by analyzing digital platforms—in this case Netflix—as surveillance-based, predictive infrastructures. Digital platforms fortify their market positions by transitioning surveillance-based assets of audience metrics into infrastructural and informational assets that set conditions for other actors and approaches at work in the domain of cultural production. We identify the central forces at play in these developments: digital platforms critically depend on proprietary surveillance data from large user bases and engage in data-structuring practices (Flyverbom and Murray 2018) that allow for predictive analytics to be a core component of their operations. Also, digital platforms engage in infrastructural development, such as Netflix’s decentralized system of video storage and content delivery, Open Connect. These meshes of user surveillance, predictive analytics, and infrastructural developments have ramifications beyond individual platforms and shape cultural production in extensive and increasingly problematic ways.

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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 analytics capabilities: analytical capability for patterns of attention, unstructured details, analytical capability, choice support capability, predictive capability, then traceability. We additionally mapped the benefits driven by big data analytics in terminology of info technology infrastructure, organizational, operational, strategic and managerial locations. Additionally, we recommend five approaches for healthcare organizations contemplating adopting big data analytics solutions. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them in drafting more effective data-driven analytics strategies.

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Ayyub, Bilal, and Karl Stambaugh. "Infrastructure Lifecycle Corrosion Management Using AI Analytics and Digital Twins." Corrosion and Materials Degradation 6, no. 2 (2025): 18. https://doi.org/10.3390/cmd6020018.

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Corrosion in infrastructure creates high-risk scenarios, and mitigation strategies are expensive, with significant annual costs globally. This paper advances the discourse of corrosion monitoring and tracking in infrastructure, emphasizing the importance of data analytics, AI, and Digital Twins (DT) for managing the infrastructure lifecycle while reducing risk and costs associated with corrosion. The non-parametric analysis of corrosion data is demonstrated to provide insights into spatial and temporal variations, helping in predictive modeling and decision-making. Strategic sampling and analysis of corrosion data help in making evidence-based maintenance decisions, reducing costs, and improving safety. AI analytics enhances the functionality of corrosion databases and Digital Twins, enabling predictive analytics and real-time simulations for better decision-making. Recommendations are provided for the implementation of AI in engineering applications, including data quantity and training resources, but offer significant potential for improved corrosion management.

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Veinblat, A. V., A. P. Sergeev, D. V. Tuliev, et al. "Development of «Energy infrastructure planning» information and analytics system." Neftyanoe khozyaystvo - Oil Industry, no. 10 (2017): 127–29. http://dx.doi.org/10.24887/0028-2448-2017-10-127-129.

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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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Chandra Thota, Ravi. "AI-Augmented Predictive Analytics for Proactive Cloud Infrastructure Management." Journal of Science & Technology 5, no. 4 (2024): 246–64. https://doi.org/10.55662/jst.2024.5407.

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Cloud computing environments involvement in advanced management strategies to ensure optimal performance, cost efficiency, and reliability. Predictive analytics based on AI- augmentation is emerged as a transformative approach to proactive cloud infrastructure management which uses machine learning models and deep learning techniques to predict system failures, optimize resource allocation, and enhance security postures. The aim of this paper is to present a complete analysis of AI-driven predictive models, highlighting anomaly detection, fault prediction, workload forecasting, and self-healing mechanisms.

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Yusuf Onifade, Abiodun, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola, Remilekun Enitan Dosumu, and Oyeronke Oluwatosin George. "Systematic Review of Marketing Analytics Infrastructure for Enabling Investor Readiness in Early-Stage Ventures." International Journal of Advanced Multidisciplinary Research and Studies 3, no. 6 (2023): 1608–20. https://doi.org/10.62225/2583049x.2023.3.6.4291.

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This systematic review explores the role of marketing analytics infrastructure in enabling investor readiness for early-stage ventures. As startups increasingly rely on data-driven decision-making, demonstrating investor readiness has become a critical factor for securing funding. This paper examines the existing literature on marketing analytics tools, techniques, and frameworks that contribute to showcasing key venture metrics, such as market traction, customer acquisition, and growth potential. We assess the types of marketing analytics infrastructure commonly employed by early-stage ventures, including customer relationship management (CRM) systems, digital marketing platforms, and predictive analytics tools. The review synthesizes findings from a wide range of studies that highlight the intersection between data analytics and the fundraising process, particularly during the pre-seed to Series A stages. By analyzing data from multiple industries, this study identifies common challenges such as limited access to sophisticated tools, the complexity of interpreting marketing data, and the misalignment between venture data and investor expectations. Moreover, it explores opportunities to improve the marketing analytics infrastructure through the use of emerging technologies such as AI and low-code/no-code platforms. Our findings emphasize the significance of demonstrating product-market fit, forecasting growth, and enhancing transparency through data-backed decision-making processes. We also identify best practices and emerging tools that can bridge the gap between data collection and investor communication, thus improving the likelihood of securing investment. The review concludes with recommendations for future research, suggesting the need for longitudinal studies and comparative analyses to better understand the long-term impact of marketing analytics on investor readiness. This review aims to contribute to the growing body of knowledge on how marketing analytics infrastructure can be strategically leveraged to enhance the funding prospects of early-stage ventures.

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Venkata Siva Prasad Maddala. "Analytics revolution in pharmaceutical industry: A technical perspective." International Journal of Science and Research Archive 14, no. 2 (2025): 451–60. https://doi.org/10.30574/ijsra.2025.14.2.0283.

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The pharmaceutical industry is experiencing a transformative revolution through the integration of advanced analytics capabilities across its value chain. This comprehensive article explores how data-driven methodologies and analytical tools are revolutionizing various aspects of pharmaceutical operations, from drug discovery to patient care. The article encompasses computational drug discovery, clinical trial optimization, manufacturing analytics, supply chain optimization, personalized medicine, sustainability analytics, and technical infrastructure requirements. The implementation of artificial intelligence, machine learning, and big data analytics has significantly improved operational efficiency, reduced costs, enhanced quality control, and accelerated drug development processes. These technological advancements have enabled more precise patient stratification, improved treatment outcomes, and fostered sustainable manufacturing practices while ensuring regulatory compliance and data security.

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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 advantage of a deep neural network to capture the complex interactions from historical states and associated load-balancing actions. The best actions are selected by maximizing the sum of rewards, taking into account short-term and long-term objectives. To increase the efficiency of the load balancing even further, we then apply algorithmic optimization approaches like genetic algorithms and ant colony optimization. Smart load-balancing strategies: These are done using an introduction of deep Q-learning algorithms, which helps in the optimization of the decision-making process of such reinforcement learning agent targeting for highly intelligent and efficient load-balancing act aggregate. Experimental results based on simulations and real-world experiments show that our framework can help network programs highly efficiently balance workloads and significantly improve the performance of the infrastructure. It can adjust to changing resource demands and conditions as well, so it should prove effective against such a dynamic environment. Overall, we present a new paradigm for implementing dynamic load balancing for AI cloud infrastructures. By combining the best of reinforcement learning and algorithmic optimization, it can improve resource utilization, delivering high-performance servers.

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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 of attack presence is performed through two-step machine learning, namely logistic regression is applied to calculate attack’s conditional probabilities with respect to the attributes, and belief propagation is applied to calculate the belief in existence of an attack based on them. Experiments are conducted to evaluate the proposed approach using well-known malware as well as in comparison with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead.

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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: customer analytics for personalization and retention; risk management for credit, market, and operational risk assessment; fraud detection through real-time monitoring and network analysis; algorithmic trading for strategy optimization and market sentiment analysis; and regulatory compliance through automated reporting and anti-money laundering systems. Despite measurable benefits, financial institutions continue to navigate substantial implementation challenges, including data quality issues, privacy concerns, infrastructure limitations, talent shortages, and ethical considerations in algorithmic decision-making. The article presents structured implementation methodologies for overcoming these obstacles, offering organizational readiness frameworks, data governance strategies, analytics maturity models, and practical roadmaps that financial institutions can adapt to their specific contexts. This article contributes both theoretical understanding and practical guidance for financial services organizations seeking to maximize value from their data assets while navigating the complex regulatory landscape and rapidly evolving technological ecosystem.

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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 and advanced analytics, cities can achieve more efficient traffic management, reduce energy consumption, streamline waste collection, and enhance emergency response systems. The study further explores the challenges associated with data privacy, security, and the integration of heterogeneous data sources. Through case studies of leading smart cities, the paper demonstrates the tangible benefits of Big Data Analytics in creating more sustainable, resilient, and livable urban environments. The findings suggest that the effective deployment of Big Data Analytics is crucial for the future development of smart cities, offering significant opportunities for improving the quality of life for urban dwellers.

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Mazher, Muhammad Ahmad, and Yasotha Nair Tramankuti. "Leveraging Business Analytics for Enhanced Decision-Making Navigating Challenges and Exploiting Opportunities." International Journal of Multidisciplinary: Applied Business and Education Research 5, no. 10 (2024): 4062–71. http://dx.doi.org/10.11594/ijmaber.05.10.21.

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Business analytics (BA) is revolutionizing how businesses use data to gain a competitive advantage. It offers opportunities for deeper customer insights, targeted marketing campaigns, improved customer satisfaction, new product and service opportunities, and optimized business processes. However, challenges like data quality, talent acquisition, and ethical considerations exist. This study reviews BA literature and discusses the challenges faced by organizations in implementing business analytics, including data availability, quality, skills and training, and technology infrastructure considerations. We conducted a study on 133 analytics professionals in Lahore, Pakistan. Data availability and quality, skills and training, and technology infrastructure all had a positive and significant impact on business analytics challenges and opportunities. The findings aim to contribute to a better understanding of business analytics, implementation challenges, and opportunities.

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Erinjogunola, Fasasi Lanre, Zamathula Sikhakhane Nwokediegwu, Rasheed O. Ajirotutu, and Rasheed Kola Olayiwola. "Enhancing Bridge Safety through AI-Driven Predictive Analytics." International Journal of Social Science Exceptional Research 4, no. 2 (2025): 10–26. https://doi.org/10.54660/ijsser.2025.4.2.10-26.

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This paper explores the integration of artificial intelligence (AI) in bridge safety and maintenance, highlighting how AI-driven predictive analytics can transform the monitoring and upkeep of aging infrastructure. As many bridges in the U.S. face deterioration due to age and environmental factors, traditional maintenance methods often fall short in predicting structural failures. By leveraging AI to analyze structural health data in real-time, we can identify potential issues before they escalate into critical failures, drastically reducing the risk of accidents and enhancing public safety. Through the implementation of advanced machine learning algorithms, AI can process vast amounts of data collected from various sensors embedded in bridge structures. This allows for continuous monitoring of key indicators such as stress, vibration, and temperature. By recognizing patterns and anomalies within this data, predictive analytics can forecast when and where maintenance will be required, enabling timely interventions. The ability to anticipate failures not only prolongs the lifespan of bridge infrastructure but also optimizes maintenance schedules, significantly reducing costs associated with emergency repairs. Drawing from my extensive experience in structural assessments and bridge maintenance, this paper presents case studies that demonstrate the practical applications of AI in civil engineering. These examples illustrate the successful implementation of AI-driven predictive analytics in real-world settings, showcasing improved safety outcomes and cost savings. Additionally, I will discuss the implications of integrating AI technologies into the existing maintenance frameworks, emphasizing how these advancements align with the national interest in adopting cutting-edge technologies to enhance public safety and infrastructure efficiency. By focusing on the intersection of AI and civil engineering, this research contributes to the growing body of knowledge on modernizing infrastructure maintenance strategies. Ultimately, the findings underscore the transformative potential of AI in enhancing bridge safety, paving the way for a more resilient and efficient infrastructure landscape.

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Rahanuma Tarannum, Sakhawat Hussain Tanim, Md Sabbir Ahmad, and Md Manarat Uddin Mithun. "Business analytics for IT infrastructure projects: Optimizing performance and security." International Journal of Science and Research Archive 14, no. 3 (2025): 783–92. https://doi.org/10.30574/ijsra.2025.14.3.0729.

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In modern IT infrastructure projects, optimizing system performance and security is crucial for ensuring reliability, efficiency, and compliance. This study explores the role of business analytics in improving IT infrastructure performance and mitigating cybersecurity risks. By utilizing real-world datasets rather than literature-based models, this research applies predictive analytics, machine learning algorithms, and business intelligence tools to enhance IT operations. The findings reveal a 26.7% reduction in CPU usage, 25% improvement in memory utilization, and a 29.2% decrease in network latency, demonstrating the effectiveness of data-driven performance optimization. Additionally, cybersecurity risk assessments using machine learning models resulted in a 14% improvement in threat detection accuracy, a 4% false positive rate, and a 75% reduction in compliance breach risks, ensuring better adherence to security frameworks like ISO 27001 and NIST. The integration of business intelligence dashboards (Tableau, Power BI) enables real-time monitoring of IT risks, enhancing decision-making and proactive threat mitigation. This study contributes to the field by providing a scalable, analytics-driven framework for IT performance enhancement and cybersecurity resilience, bridging the gap between operational efficiency and security risk management. Future research should explore advanced AI-driven automation and real-time adaptive security measures to further strengthen IT infrastructure

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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 digital transformation.   This research aims to bridge the gap between IoT infrastructure and big data analytics by demanding integration. Descriptive strategies involve information processing, pattern recognition, and using intuition to make informed decisions. Seamless integration of IoT devices with advanced analytics tools to facilitate digital transformation across businesses. Highlighting the importance of combining  IoT  resources  with  big  data analytics,  this combination is essential to drive change, support innovation, increase efficiency, and gain competitive advantage. The study demonstrates the important role of IoT and big data analytics in shaping the digital environment by delving into the connection between them to drive change. It considers an integrated strategy that integrates IoT infrastructure with advanced analytics tools to unlock the potential of digital transformation. This research lays the foundation for new solutions, improved processes, and leveraging the power of data to move businesses into the digital age.

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Heinemann, Birte, Matthias Ehlenz, Sergej Görzen, and Ulrik Schroeder. "xAPI Made Easy: A Learning Analytics Infrastructure for Interdisciplinary Projects." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 14 (2022): 99–113. http://dx.doi.org/10.3991/ijoe.v18i14.35079.

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Learning Analytics provides plenty of pedagogical uses. However, the integration of learning analytics must be accompanied by different perspectives: technical, organizational, and pedagogical. At this point, there are still gaps, e.g., the need to connect the various stakeholders and support the systematic, structured, and sustainable process. This paper presents different approaches to making the learning data standard xAPI for interdisciplinary projects easier by working on other starting points. Starting with a basic infrastructure to support the interdisciplinary collection of definitions for the standardized data format, it continues with a graphical user interface supporting different stakeholders. A modular tool for quickly connecting programming IDEs with the vocabulary is also presented. Last, a connector is shown for easier multi-modal data management using virtual reality as an example.

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Rani, Dr Geeta, Ananya Singh, and Siddharth Singh. "IntelliHome: The Automated Household Infrastructure." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 414–22. http://dx.doi.org/10.22214/ijraset.2024.57192.

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Abstract: The rapid advancement of technology in recent years has ushered in an era of unprecedented connectivity and automation in our daily lives. One of the most promising developments in this domain is the concept of IntelliHome – an integrated and intelligent household infrastructure that seamlessly combines smart devices, artificial intelligence, and data analytics to enhance convenience, efficiency, and sustainability within the home environment. This research paper delves into the multifaceted realm of IntelliHome, exploring its technological underpinnings, potential benefits, challenges, and societal implications. The paper begins by elucidating the fundamental components of an IntelliHome, encompassing a spectrum of smart devices such as thermostats, lighting systems, security systems, and appliances, all interconnected through the Internet of Things (IoT). It discusses the pivotal role of artificial intelligence in orchestrating these devices, enabling autonomous decision-making, predictive analytics, and adaptive customization to cater to the unique needs and preferences of each household. Furthermore, the research paper delves into the manifold benefits of an IntelliHome, including energy conservation, increased security, enhanced convenience, and improved quality of life. It examines how IntelliHome systems can reduce carbon footprints, optimize resource utilization, and contribute to the development of sustainable smart cities. Finally, the research paper contemplates the broader societal implications of IntelliHome technology. It explores how these systems may redefine the boundaries of work, leisure, and domesticity, and how they may influence family dynamics, social interactions, and community engagement. It also investigates the economic aspects and economic impacts.

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Jantti, Margie, and Jennifer Heath. "What role for libraries in learning analytics?" Performance Measurement and Metrics 17, no. 2 (2016): 203–10. http://dx.doi.org/10.1108/pmm-04-2016-0020.

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Purpose – The purpose of this paper is to provide an overview of the development of an institution wide approach to learning analytics at the University of Wollongong (UOW) and the inclusion of library data drawn from the Library Cube. Design/methodology/approach – The Student Support and Education Analytics team at UOW is tasked with creating policy, frameworks and infrastructure for the systematic capture, mapping and analysis of data from the across the university. The initial data set includes: log file data from Moodle sites, Library Cube, student administration data, tutorials and student support service usage data. Using the learning analytics data warehouse UOW is developing new models for analysis and visualisation with a focus on the provision of near real-time data to academic staff and students to optimise learning opportunities. Findings – The distinct advantage of the learning analytics model is that the selected data sets are updated weekly, enabling near real-time monitoring and intervention where required. Inclusion of library data with the other often disparate data sets from across the university has enabled development of a comprehensive platform for learning analytics. Future work will include the development of predictive models using the rapidly growing learning analytics data warehouse. Practical implications – Data warehousing infrastructure, the systematic capture and exporting of relevant library data sets are requisite for the consideration of library data in learning analytics. Originality/value – What was not anticipated five years ago when the Value Cube was first realised, was the development of learning analytic services at UOW. The Cube afforded University of Wollongong Library considerable advantage: the framework for data harvesting and analysis was established, ready for inclusion within learning analytics data sets and subsequent reporting to faculty.

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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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Kramis, Marc, Cedric Gabathuler, Sara Irina Fabrikant, and Marcel Waldvogel. "An XML-based Infrastructure to Enhance Collaborative Geographic Visual Analytics." Cartography and Geographic Information Science 36, no. 3 (2009): 281–93. http://dx.doi.org/10.1559/152304009788988305.

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Volkov, V. I. "Use of expert assessment and analytics in Russia’s innovation infrastructure." Scientific and Technical Information Processing 36, no. 3 (2009): 160–69. http://dx.doi.org/10.3103/s014768820903006x.

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Borri, Vinícius de Marchi, Fernando Gasi, and Alessandra Akkari. "Analytics Hierarchy Process for Decision-making in Network Infrastructure Replacement." International Journal of Advanced Engineering Research and Science 9, no. 9 (2022): 374–77. http://dx.doi.org/10.22161/ijaers.99.39.

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Among all methodologies to support multi-criteria decision making, the Analytics Hierarchy Process is widely used in the most diverse situations. Different authors treat it as one of the best alternatives when there is a need to introduce subjective criteria within the decision-making process, in addition to having adaptability to situations with a high number of alternatives using ratings. In this work, a comparison was made between the ranking results of 30 areas for replacing the network infrastructure with fiber optic networks through numerical criteria and reordering by decision makers and through a model using AHP with ratings. As a result, a similar final ranking can be seen, which enables the use of AHP as a tool to improve the decision-making process for this situation.

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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 recourses to deal with a large volume of data. This limitation of resources affects the performance of the tools in terms of execution time. Therefore, in this work, we proposed a model in which different data analytics tools share a common infrastructure to provide data independence and resource sharing environment, i.e. the proposed model shares common (Hybrid) Hadoop Distributed File System (HDFS) between three Name-Node (Master Node), three Data-Node and one Client-node, which works under the DeMilitarized zone (DMZ). To realize this model, we have implemented Mahout, R-Hadoop and Splunk sharing a common HDFS. Further using our model, we run [Formula: see text]-means clustering, Naïve Bayes and recommender algorithms on three different datasets, movie rating, newsgroup, and Spam SMS dataset, representing structured, semi-structured and unstructured, respectively. Our model selected the appropriate tool, e.g. Mahout to run on the newsgroup dataset as other tools cannot run on this data. This shows that our model provides data independence. Further results of our proposed model are compared with the legacy (individual) model in terms of execution time and scalability. The improved performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model.

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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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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 state-of-the-art and provide an overview of Big Data architectures used in LA. Based on the examination of the results, we identify common architectural components and technologies and present them in the form of a mind map. Linking the findings, we are proposing an initial approach towards a Trusted and Interoperable Learning Analytics Infrastructure (TIILA).

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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 demonstrated improved healthcare data analytics skills over time, related to concomitant increases in their healthcare data analytics-related learning and performance goals. This study suggests that healthcare organizations aiming at successfully implementing a new data analytics infrastructure should provide well-designed training that enables trainees to develop specific learning and performance goals as well as improve relevant skills and ability to use new tools.

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Dakshaja Prakash Vaidya. "AI and resilient cloud infrastructure in healthcare." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4430–36. https://doi.org/10.30574/wjarr.2025.26.2.2069.

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The transformative convergence of artificial intelligence and resilient cloud infrastructure within healthcare environments represents a fundamental shift in medical service delivery, data management, and patient care administration. The digital evolution occurring across healthcare institutions has established new frameworks for handling clinical information at unprecedented scale and complexity. Cloud infrastructure provides the foundation through multi-tier architectures that balance security requirements with accessibility needs, while sophisticated storage frameworks accommodate the exponential growth in diverse clinical data types. Interoperability standards facilitate seamless data exchange across previously siloed systems, creating comprehensive patient records that enable holistic analytics. AI integration enhances patient care through predictive analytics that identify deterioration risks before clinical manifestation, diagnostic support systems that analyze medical images with remarkable precision, and treatment optimization frameworks that recommend personalized intervention strategies. Resilient system design incorporating high-availability architectures, robust cybersecurity frameworks, and comprehensive disaster recovery protocols ensures continuous operation even during infrastructure challenges. Looking forward, edge computing deployments and federated learning approaches promise to further enhance system capabilities, while evolving regulatory frameworks emphasize algorithmic transparency and validation methodologies.

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Moscoso-Zea, Oswaldo, Jorge Castro, Joel Paredes-Gualtor, and Sergio Luján-Mora. "A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics for Knowledge Management in Education." IEEE Access 7 (March 20, 2019): 38778–88. https://doi.org/10.1109/ACCESS.2019.2906343.

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Advances in science and technology, the Internet of Things, and the proliferation of mobile apps are critical factors to the current increase in the amount, structure, and size of information that organizations have to store, process, and analyze. Traditional data storages present technical deficiencies when handling huge volumes of data and are not adequate for process modeling and business intelligence; to cope with these deficiencies, new methods and technologies have been developed under the umbrella of big data. However, there is still the need in higher education institutions (HEIs) of a technological tool that can be used for big data processing and knowledge management (KM). To overcome this issue, it is essential to develop an information infrastructure that allows the capturing of knowledge and facilitates experimentation by having cleaned and consistent data. Thus, this paper presents a hybrid information infrastructure for business intelligence and analytics (BI&A) and KM based on an educational data warehouse (EDW) and an enterprise architecture (EA) repository that allows the digitization of knowledge and empowers the visualization and the analysis of dissimilar organizational components as people, processes, and technology. The proposed infrastructure was created based on research and will serve to run different experiments to analyze educational data and academic processes and for the creation of explicit knowledge using different algorithms and methods of educational data mining, learning analytics, online analytical processing (OLAP), and EA analytics.

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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 layouts, enhance marketing strategies, and improve overall operational efficiency. The article also addresses critical technical considerations such as privacy, security, and system scalability, while exploring future developments in retail analytics. Through detailed examination of multiple data sources and advanced analytical techniques, this article provides valuable insights into the practical applications and benefits of AI-powered retail analytics, offering retailers a roadmap for implementing data-driven decision-making processes to enhance customer experience and business performance.

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Petri, Ioan, Omer Rana, Yacine Rezgui, and Fodil Fadli. "Edge HVAC Analytics." Energies 14, no. 17 (2021): 5464. http://dx.doi.org/10.3390/en14175464.

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Integrating data analytics, optimisation and dynamic control to support energy services has seen significant interest in recent years. Larger appliances used in an industry context are now provided with Internet of Things (IoT)-based interfaces that can be remotely monitored, with some also provided with actuation interfaces. The combined use of IoT and edge computing enables connectivity between energy systems and infrastructure, providing the means to implement both energy efficiency/optimisation and cost reduction strategies. We investigate the economic implications of harnessing IoT and edge/cloud technologies to support energy management for HVAC (Heating, Ventilation and Air Conditioning) systems in buildings. In particular, we evaluate the cost savings for building operations through energy optimisation. We use a real use case for energy optimisation as identified in the EU “Sporte2” project (focusing on the energy optimisation of sports facilities) and explore several scenarios in relation to costs and energy optimisation.

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Journal articles on the topic 'Analytics infrastructure'

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