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Relevant bibliographies by topics / Data Warehouse Modernization

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Academic literature on the topic 'Data Warehouse Modernization'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Data Warehouse Modernization"

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Seethala, Srinivasa Chakravarthy. "Data Warehouse Modernization for Insurance: Integrating AI and Cloud Technologies." International Journal of Scientific Research and Engineering Trends 7, no. 6 (2021): 3654–56. http://dx.doi.org/10.61137/ijsret.vol.10.issue5.794.

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Lin, Hung-Lung, Cheng-Chung Cho, Yu-Yu Ma, Ying-Qing Hu, and Ze-Hui Yang. "OPTIMIZATION PLAN FOR EXCESS WAREHOUSE STORAGE IN E-COMMERCE–BASED PLANT SHOPS: A CASE STUDY FOR CHINESE PLANT INDUSTRIAL." Journal of Business Economics and Management 20, no. 5 (2019): 897–919. http://dx.doi.org/10.3846/jbem.2019.10188.

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The rapid development of e-commerce in China has played a critical role in the development of the national economy and ongoing modernization. The plant industry is unique among industries that employ e-commerce sales models because its products exhibit special characteristics such as high death and damage rates. Therefore, its e-commerce and logistical requirements are stricter than in other industries and, as a result, excess warehouse storage can be extremely difficult for e-commerce–based plant shops to manage. Numerous studies have indicated the need to identify a product’s most up-to-date

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Srinivasa, Chakravarthy Seethala. "Data Warehouse Modernization with AI: A Strategic Path for the Retail Industry." European Journal of Advances in Engineering and Technology 6, no. 2 (2019): 117–19. https://doi.org/10.5281/zenodo.14168854.

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The retail industry is undergoing a significant transformation, driven by the need for enhanced operational efficiency and improved customer experiences. This paper explores the critical role of artificial intelligence (AI) in modernizing data warehouses within the retail sector. We examine how AI addresses key challenges such as data integration, real-time analytics, and inventory management. The study investigates various applications of AI and proposes a framework for successful implementation. Our findings suggest that AI-driven data warehousing offers unprecedented opportunities for opera

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Seethala, Srinivasa Chakravarthy. "AI-Driven Data Warehouse Modernization in the Healthcare Sector: A Blueprint for Efficiency Modernizing Legacy Data Warehouses with AI-Enhanced Workflows." International Journal of Scientific Research and Engineering Trends 2, no. 6 (2016): 169–72. http://dx.doi.org/10.61137/ijsret.vol.2.issue6.135.

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Srinivasa, Chakravarthy Seethala, and Krishna Chaithanya Seethala Sai. "AI-Powered Data Warehouse Modernization for Utilities: Achieving Real-Time Insights and Efficiency." Journal of Scientific and Engineering Research 9, no. 12 (2022): 188–90. https://doi.org/10.5281/zenodo.14059569.

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The utilities sector is at a pivotal moment, driven by the need to integrate advanced technologies to enhance operational efficiency and customer service. This paper explores the transformative potential of AI-powered data warehouse modernization in utilities, focusing on achieving real-time insights and operational efficiency. By leveraging AI and cloud computing, utilities can address challenges such as data integration, predictive maintenance, demand forecasting, and energy management. Our study highlights key applications and proposes a framework for successful implementation. The findings

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Cecil, Prashanth. "Technology Integration in Non-Robotic Warehouses: Implementing LiDAR and IoT for Efficiency Gains." International Journal of Engineering and Computer Science 12, no. 11 (2023): 25982–6004. https://doi.org/10.18535/ijecs.v12i11.4749.

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The rapid evolution of global supply chains and increasing consumer demands have intensified the need for more efficient, responsive, and accurate warehousing operations. While fully automated warehouses, powered by robotics and artificial intelligence, have demonstrated the potential to revolutionize logistics, the high implementation costs, complexity, and infrastructural overhaul required pose significant barriers for many organizations—particularly small and medium-sized enterprises (SMEs). In response, this study explores the integration of two emerging yet accessible technologies—Light D

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Aulin, Victor, Sergii Tyshchenko, and Andriy Hrynkiv. "Innovative Solutions in Warehouse Logistics." Central Ukrainian Scientific Bulletin. Technical Sciences 1, no. 10(41) (2024): 270–85. https://doi.org/10.32515/2664-262x.2024.10(41).1.270-285.

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The article discusses current trends in warehouse logistics, such as automation, digitalization, omnichannel, environmental sustainability, and the growth of e-commerce. The main challenges of the industry include high automation costs, insufficient staff qualifications to work with innovative technologies, difficulties in adapting to global changes in supply chains, and the need to comply with environmental standards. The article analyzes the latest research on the use of intelligent systems, big data, artificial intelligence (AI) and the Internet of Things (IoT) to optimize warehouse operati

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Barton, Martin, Roman Budjac, Pavol Tanuska, Ivan Sladek, and Martin Nemeth. "Advancing Small and Medium-Sized Enterprise Manufacturing: Framework for IoT-Based Data Collection in Industry 4.0 Concept." Electronics 13, no. 13 (2024): 2485. http://dx.doi.org/10.3390/electronics13132485.

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Currently, industrial engineers are witnessing a rapid development of innovation in modern industry and the integration of critical elements of Industry 4.0 into production processes in order to remain competitive. Such changes are characterized by a large amount of effort and financial resources. To successfully deploy these changes requires not only the purchase of hardware and software but also the training of employees and the modification of the company’s organizational structure. The main objective of the article is to propose a framework for the modernization of SMEs to a level close to

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Srinivasa, Chakravarthy Seethala. "Revolutionizing Data Warehouses in Manufacturing: Big Data-Infused Automation for ETL and Beyond." Journal of Scientific and Engineering Research 4, no. 10 (2017): 525–28. https://doi.org/10.5281/zenodo.14169254.

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The manufacturing industry is witnessing a transformative shift as data warehouses evolve from static data storage solutions to dynamic, big data-infused infrastructures. Leveraging automation in Extract, Transform, Load (ETL) processes, big data has revolutionized data accessibility, scalability, and analytics in manufacturing. This paper explores the integration of big data and automation in manufacturing data warehouses, highlighting innovations in ETL, data processing, and analytics capabilities. By examining the foundational elements and potential of these technologies, this article provi

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Zazulya, Alexander, Valentin Makarov, Olga Makarova, and Svetlana Gasparyan. "IMPROVING THE ORGANIZATION OF FOOD STORAGE IN WAREHOUSES IN THE MEDICAL AND CORRECTIONAL INSTITUTION OF THE PENITENTIARY SYSTEM." SCIENCE IN THE CENTRAL RUSSIA, no. 5 (October 31, 2022): 86–93. http://dx.doi.org/10.35887/2305-2538-2022-5-86-93.

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Topical directions for improving the organization of warehouse management in a medical correctional institution are considered. Much attention is paid to the shelf life of vegetable products intended for catering for persons in a medical institution of the penitentiary system. The warehouse modernization system associated with air conditioning is proposed in order to solve the problem areas of vegetable storage. The advantages of the proposed system are analyzed, such as: drying vegetables, moisturizing in a certain period, cooling, reducing the natural loss during storage, temperature conditi

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Book chapters on the topic "Data Warehouse Modernization"

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Patel, Monika, and Dhiren B. Patel. "Data Warehouse Modernization Using Document-Oriented ETL Framework for Real Time Analytics." In Rising Threats in Expert Applications and Solutions. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1122-4_5.

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Blanco, Carlos, Ricardo Pérez-Castillo, Arnulfo Hernández, Eduardo Fernández-Medina, and Juan Trujillo. "Towards a Modernization Process for Secure Data Warehouses." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03730-6_3.

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Mazón, Jose-Norberto, and Juan Trujillo. "A Model Driven Modernization Approach for Automatically Deriving Multidimensional Models in Data Warehouses." In Conceptual Modeling - ER 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75563-0_6.

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Conference papers on the topic "Data Warehouse Modernization"

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Borozdin, Sergey Olegovich, Anatoly Nikolaevich Dmitrievsky, Nikolai Alexandrovich Eremin, et al. "Drilling Problems Forecast Based on Neural Network." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30984-ms.

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Abstract This paper poses and solves the problem of using artificial intelligence methods for processing big volumes of geodata from geological and technological measurement stations in order to identify and predict complications during well drilling. Big volumes of geodata from the stations of geological and technological measurements during drilling varied from units to tens of terabytes. Digital modernization of the life cycle of well construction using machine learning methods contributes to improving the efficiency of drilling oil and gas wells. The clustering of big volumes of geodata fr

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