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Journal articles on the topic 'Addresses (data processing)'

Author: Grafiati

Published: 29 July 2024

Last updated: 31 July 2025

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1

Johnson, Sarah L. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 1, no. 02 (2025): 1–11. https://doi.org/10.63665/ijicsitr.v1i02.04.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discu

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Chanda, Deepak. "Automated ETL Pipelines for Modern Data Warehousing: Architectures, Challenges, and Emerging Solutions." Eastasouth Journal of Information System and Computer Science 1, no. 03 (2024): 209–12. https://doi.org/10.58812/esiscs.v1i03.523.

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The paper addresses the evolution of automated Extract, Transform, Load (ETL) pipelines in contemporary data warehousing environments, highlighting their essential role in enabling timely analytics and business intelligence. Recent architectural approaches like cloud-native ETL, stream processing architectures, and metadata-driven automation are addressed in the context of increasing data volume and variety. The article addresses typical challenges like schema evolution management, data quality assurance, and cross-platform integration in the context of discussing novel solutions based on leve

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Researcher. "REAL-TIME DATA PROCESSING IN MICROSERVICES ARCHITECTURES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 760–73. https://doi.org/10.5281/zenodo.14228620.

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Real-time data processing in modern distributed systems has evolved significantly, transforming how organizations across various sectors handle operational demands. This comprehensive article explores the fundamental aspects of real-time processing in microservices architectures, examining key technological advancements, implementation strategies, and architectural patterns. The article investigates the impact of event-driven architectures, message brokers, and stream processing technologies while detailing best practices for maintaining data consistency and system performance. Examining cloud

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Romanchuk, Vitaliy. "Mathematical support and software for data processing in robotic neurocomputer systems." MATEC Web of Conferences 161 (2018): 03004. http://dx.doi.org/10.1051/matecconf/201816103004.

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The paper addresses classification and formal definition of neurocomputer systems for robotic complexes, based on the types of associations among their elements. We suggest analytical expressions for performance evaluation in neural computer information processing, aimed at development of methods, algorithms and software that optimize such systems.

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Hahanov, V. I., V. H. Abdullayev, S. V. Chumachenko, E. I. Lytvynova, and I. V. Hahanova. "IN-MEMORY INTELLIGENT COMPUTING." Radio Electronics, Computer Science, Control, no. 1 (April 2, 2024): 161. http://dx.doi.org/10.15588/1607-3274-2024-1-15.

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Context. Processed big data has social significance for the development of society and industry. Intelligent processing of big data is a condition for creating a collective mind of a social group, company, state and the planet as a whole. At the same time, the economy of big data (Data Economy) takes first place in the evaluation of processing mechanisms, since two parameters are very important: speed of data processing and energy consumption. Therefore, mechanisms focused on parallel processing of large data within the data storage center will always be in demand on the IT market. Objec

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Gururaj T. and Siddesh G. M. "Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–18. http://dx.doi.org/10.4018/ijcini.20211001.oa38.

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In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer count

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Sun, Xihuang, Peng Liu, Yan Ma, Dingsheng Liu, and Yechao Sun. "Streaming Remote Sensing Data Processing for the Future Smart Cities." International Journal of Distributed Systems and Technologies 7, no. 1 (2016): 1–14. http://dx.doi.org/10.4018/ijdst.2016010101.

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The explosion of data and the increase in processing complexity, together with the increasing needs of real-time processing and concurrent data access, make remote sensing data streaming processing a wide research area to study. This paper introduces current situation of remote sensing data processing and how timely remote sensing data processing can help build future smart cities. Current research on remote sensing data streaming is also introduced where the three typical and open-source stream processing frameworks are introduced. This paper also discusses some design concerns for remote sen

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Nguyen, Minh Duc. "A Scientific Workflow System for Satellite Data Processing with Real-Time Monitoring." EPJ Web of Conferences 173 (2018): 05012. http://dx.doi.org/10.1051/epjconf/201817305012.

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This paper provides a case study on satellite data processing, storage, and distribution in the space weather domain by introducing the Satellite Data Downloading System (SDDS). The approach proposed in this paper was evaluated through real-world scenarios and addresses the challenges related to the specific field. Although SDDS is used for satellite data processing, it can potentially be adapted to a wide range of data processing scenarios in other fields of physics.

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Prabagar, S., Vinay K. Nassa, Senthil V. M, Shilpa Abhang, Pravin P. Adivarekar, and Sridevi R. "Python-based social science applications’ profiling and optimization on HPC systems using task and data parallelism." Scientific Temper 14, no. 03 (2023): 870–76. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.48.

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This research addresses the pressing need to optimize Python-based social science applications for high-performance computing (HPC)systems, emphasizing the combined use of task and data parallelism techniques. The paper delves into a substantial body of research,recognizing Python’s interpreted nature as a challenge for efficient social science data processing. The paper introduces a Pythonprogram that exemplifies the proposed methodology. This program uses task parallelism with multi-processing and data parallelismwith dask to optimize data processing workflows. It showcases how researchers c

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Krishnamurthi, Rajalakshmi, Adarsh Kumar, Dhanalekshmi Gopinathan, Anand Nayyar, and Basit Qureshi. "An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques." Sensors 20, no. 21 (2020): 6076. http://dx.doi.org/10.3390/s20216076.

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision

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Chatzakis, Manos, Panagiota Fatourou, Eleftherios Kosmas, Themis Palpanas, and Botao Peng. "Odyssey: A Journey in the Land of Distributed Data Series Similarity Search." Proceedings of the VLDB Endowment 16, no. 5 (2023): 1140–53. http://dx.doi.org/10.14778/3579075.3579087.

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This paper presents Odyssey, a novel distributed data-series processing framework that efficiently addresses the critical challenges of exhibiting good speedup and ensuring high scalability in data series processing by taking advantage of the full computational capacity of modern distributed systems comprised of multi-core servers. Odyssey addresses a number of challenges in designing efficient and highly-scalable distributed data series index, including efficient scheduling, and load-balancing without paying the prohibitive cost of moving data around. It also supports a flexible partial repli

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Šprem, Šimun, Nikola Tomažin, Jelena Matečić, and Marko Horvat. "Building Advanced Web Applications Using Data Ingestion and Data Processing Tools." Electronics 13, no. 4 (2024): 709. http://dx.doi.org/10.3390/electronics13040709.

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Today, advanced websites serve as robust data repositories that constantly collect various user-centered information and prepare it for subsequent processing. The data collected can include a wide range of important information from email addresses, usernames, and passwords to demographic information such as age, gender, and geographic location. User behavior metrics are also collected, including browsing history, click patterns, and time spent on pages, as well as different preferences like product selection, language preferences, and individual settings. Interactions, device information, tra

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Sikarwar, Tarika Singh, Abhijeet Singh Chauhan, Nidhi Jain, and Harshita Mathur. "Application of Predictive Analytics in IOT Data Processing." Indian Journal of Information Sources and Services 15, no. 2 (2025): 340–48. https://doi.org/10.51983/ijiss-2025.ijiss.15.2.42.

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Working with predictive models mean hugeopportunities for the business-using real-time data, while therapid growth of the Internet of Things-(IoT)-provides uniqueopportunities and hurdles to business. Predictive analytics hasemerged as a cutting-edge approach in the analysis of vast andintricate IoT datasets, using statistics, machine learningalgorithms, and artificial intelligence. In other words, thischapter elaborates on how predictive analytics could fit into IoTdata management as an enabler for proactive decision-makingand outlines its use in forecasting trends, behaviours, andoutcomes.Us

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Datyev, I. O., A. М. Fedorov, and A. A. Reviakin. "Focused collection and processing of open social media data." Ontology of designing 14, no. 4 (2024): 569–81. http://dx.doi.org/10.18287/2223-9537-2024-14-4-569-581.

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The article addresses the development of data collection technologies and the complexities that challenge this process. Methods for focusing at various levels are discussed, ranging from managing scanning boundaries to leveraging diverse properties of web pages. Here, the term "focusing" is used to accurately reflect the specific characteristics of targeted data collection and processing of open social media data. This process is multi-stage, employing adaptive control mechanisms that adjust dynamically toward the specified objective. During control, these defined constraints are either narrow

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Jain, Pritesh. "QUANTUM-ENHANCED EDGE COMPUTING FOR REAL-TIME DATA PROCESSING IN AUTONOMOUS SYSTEMS." COMPUSOFT: An International Journal of Advanced Computer Technology 10 (September 21, 2021): 3984–86. https://doi.org/10.5281/zenodo.15026322.

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The proliferation of Internet of Things (IoT) devices and autonomous systems has necessitated advancements in realtime data processing capabilities. Edge computing addresses latency and bandwidth issues by processing data closer to the source. However, traditional edge computing approaches struggle with the computational demands of complex algorithms, especially in autonomous systems. This paper introduces a novel approach that leverages quantum computing principles to enhance edge computing frameworks. We propose a hybrid architecture combining quantum-enhanced processing units with classical

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Ridwan Kolapo, Fatima Mohammed Kawu, Aminu Dalhatu Abdulmalik, Ubong Anietie Edem, Maureen Atisi Young, and Emmanuel Chukwudinma Mordi. "Edge computing: Revolutionizing data processing for IoT applications." International Journal of Science and Research Archive 13, no. 2 (2024): 023–29. http://dx.doi.org/10.30574/ijsra.2024.13.2.2082.

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Edge computing is emerging as a transformative solution for managing the vast amounts of data generated by the Internet of Things (IoT). By decentralizing data processing and bringing computation closer to the data source, edge computing addresses critical limitations of traditional cloud computing, including latency, bandwidth constraints, and security vulnerabilities. This review explores the key benefits of edge computing, such as reduced latency, bandwidth optimization, improved reliability, enhanced data privacy, and scalability. It discusses the architecture and components of edge comput

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Researcher. "ARTIFICIAL INTELLIGENCE IN DYNAMIC DATA TRANSFORMATION: A FRAMEWORK FOR ENTERPRISE INTEGRATION AND OPTIMIZATION." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 1255–69. https://doi.org/10.5281/zenodo.14370114.

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The exponential growth in data volume and complexity has created an urgent need for more sophisticated approaches to data transformation in enterprise environments. This article presents a comprehensive framework for implementing artificial intelligence (AI) in dynamic data transformation processes, addressing key challenges in data quality, schema evolution, and real-time processing. Through multiple case studies across different industries, we examine the implementation of machine learning algorithms, natural language processing, and predictive analytics in automating and optimizing dat

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Venkata Nagendra Kumar Kundavaram. "Event-Driven Data Pipelines : A Cloud-Based Approach to Real-Time Data Processing." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 364–69. http://dx.doi.org/10.32628/cseit24106183.

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This article comprehensively analyzes event-driven data pipelines in cloud computing environments, examining their architecture, implementation considerations, and real-world applications. The article explores the fundamental components of event-driven systems, from event sources and message brokers to processing layers, while evaluating their performance characteristics and reliability mechanisms. Through detailed analysis of system architectures, we investigate the integration of various cloud services and their role in enabling scalable, real-time data processing. The article demonstrates h

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Researcher. "THE IMPACT OF 5G ON CLOUD DATA ARCHITECTURES." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 879–89. https://doi.org/10.5281/zenodo.14046052.

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This article explores the transformative impact of 5G technology on cloud data architectures. It examines how 5G's high-speed, low-latency capabilities are driving significant changes in data processing paradigms, database designs, and real-time applications. The article focuses on key areas including edge computing acceleration, real-time data processing, and the evolution of database architectures. It also addresses the challenges of data security, privacy, and energy efficiency in 5G networks. The article concludes by discussing future directions and the potential for innovation in cloud co

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Vijaya, Dr V. Krishna. "INVOICE DATA EXTRACTION USING OCR TECHNIQUE." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29981.

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Traditional invoice processing involves manual entry of data, leading to human errors, delays,and increased operational costs. The lack of automation results in inefficiencies, hindering organizations from promptly accessing critical financial information. This research addresses the pressing need for a reliable OCR-based solution to automate invoice data extraction, ultimately improving accuracy, reducing processing time, and enhancing overall business productivity. The project aims to automate invoice data extraction through Optical Character Recognition (OCR) techniques. Leveraging advanced

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Avinash Dulam. "Enhancing data processing with Apache spark: A technical deep dive." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1279–84. https://doi.org/10.30574/wjaets.2025.15.3.0910.

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Apache Spark has revolutionized big data processing by introducing a unified computing framework that addresses the challenges of distributed data processing, real-time analytics, and machine learning at scale. The framework's architecture, built on Resilient Distributed Datasets (RDDs), enables fault-tolerant parallel operations while providing sophisticated optimization techniques for enhanced performance. Through advanced features like Structured Streaming, DataFrame abstractions, and MLlib integration, Spark offers comprehensive solutions for modern data processing needs, from batch proces

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Li, Wenqi, Pengyi Zhang, and Jun Wang. "Humanities Scholars' Understanding of Data and the Implications for Humanities Data Curation." Proceedings of the Association for Information Science and Technology 60, no. 1 (2023): 1034–36. http://dx.doi.org/10.1002/pra2.936.

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ABSTRACTThis study addresses the need for a shared understanding of humanities data to enhance data curation. Through interviews with 27 scholars, it identifies two ways scholars conceptualize data ‐ by format or role in research. It highlights three unique aspects: diverse requirements of materiality and processing levels, significance of authorship and perspective, and the dual role of tertiary sources. The study suggests prioritizing provenance, facilitating data documentation, curating tertiary sources for wider use, and establishing scholarly communication mechanisms for effective data cu

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Gopinath Govindarajan. "Building a strong foundation in data engineering: a comprehensive guide for aspiring data analysts." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 3901–7. https://doi.org/10.30574/wjarr.2025.26.1.1508.

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This comprehensive article explores the fundamental aspects of building a strong foundation in data engineering, focusing on the transformation of data processing and management in modern organizations. The article examines the evolution of data engineering practices, highlighting the integration of artificial intelligence, cloud technologies, and automated workflows in contemporary data architectures. It investigates core technical foundations, including database management, SQL optimization, and Python programming, while analyzing the impact of cloud-native services and distributed computing

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Krupal Gangapatnam. "Automated data anonymization tools to comply with GDPR regulations, processing billions of data points stored across multiple cloud environments." International Journal of Science and Research Archive 14, no. 1 (2025): 787–96. https://doi.org/10.30574/ijsra.2025.14.1.0116.

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The rapid evolution of data processing demands has led to innovative approaches in enterprise-scale data anonymization and protection. This comprehensive examination explores the implementation of Delphix across diverse cloud environments, focusing on its technical architecture, performance metrics, and compliance features. The platform demonstrates exceptional capabilities in handling sensitive data through advanced machine learning algorithms and sophisticated processing pipelines. The architecture incorporates robust security mechanisms, parallel processing capabilities, and intelligent res

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BRIA, Vasile, and Marius BODOR. "Automated Processing of Mechanical Test Data for Various Composite Materials Using MATLAB." Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science 48, no. 1 (2025): 28–38. https://doi.org/10.35219/mms.2025.1.05.

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The behaviour of composite materials during the mechanical testing process might exhibit, in some situations, very different patterns compared to those of conventional materials. This is why the eventual automation of data processing might require additional steps towards obtaining realistic results from mechanical testing. The present work addresses this issue by proposing an algorithm written using MATLAB software and applying it in processing data from mechanical testing of selected composite materials with various compositions and behaviours.

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Moustakidis, Serafeim, Athanasios Anagnostis, Apostolos Chondronasios, Patrik Karlsson, and Kostas Hrissagis. "Excitation-invariant pre-processing of thermographic data." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 4 (2018): 435–46. http://dx.doi.org/10.1177/1748006x18770888.

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There is a large number of industries that make extensive use of composite materials in their respective sectors. This rise in composites’ use has necessitated the development of new non-destructive inspection techniques that focus on manufacturing quality assurance, as well as in-service damage testing. Active infrared thermography is now a popular nondestructive testing method for detecting defects in composite structures. Non-uniform emissivity, uneven heating of the test surface, and variation in thermal properties of the test material are some of the crucial factors in experimental thermo

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Venkata, Gummadi. "Designing a Scalable Architecture for Customer Data Engineering Platform on Cloud Infrastructure: A Comprehensive Framework." Journal of Scientific and Engineering Research 10, no. 12 (2023): 243–51. https://doi.org/10.5281/zenodo.14012383.

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The exponential growth of customer data in modern enterprises has created unprecedented challenges in data engineering, necessitating architectures capable of handling petabyte-scale processing while maintaining real-time analytics capabilities. This paper presents a comprehensive architectural framework for designing and implementing scalable customer data engineering platforms utilizing cloud infrastructure. The proposed architecture addresses critical challenges including real-time data processing, horizontal scalability, data governance, and security considerations. Through rigorous experi

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Suchit, Kumar Vyas, and Satish Kumar Dr. "Critical issues of data security and privacy of mobile cloud computing." International Journal of Trends in Emerging Research and Development 2, no. 2 (2024): 165–70. https://doi.org/10.5281/zenodo.13118086.

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Cloud computing is a rapidly evolving technology that provides shared processing resources and data to computers and other devices on demand. Mobile computing, on the other hand, facilitates the transmission of data, voice, and video. The convergence of these technologies has given rise to Mobile Cloud Computing (MCC), a concept that not only addresses the limitations of mobile computing but also integrates cloud computing into mobile environments to tackle challenges related to performance, security, and resource constraints. MCC is gaining momentum as the number of mobile users continues to

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Mohammed-Javed Padinhakara. "Vehicular data management at scale: Architectural frameworks for cars as mobile data centers." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 752–58. https://doi.org/10.30574/wjaets.2025.15.3.0940.

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The emerging paradigm of modern vehicles as sophisticated mobile data centers generates unprecedented volumes of telemetry, sensor, and interaction data that require novel management approaches. The architectural framework addresses dual requirements of edge processing for latency-sensitive applications and cloud infrastructure for deeper analytics and model development. Vehicle-to-everything communication protocols integrate with software-defined networks and distributed ledger technologies to ensure secure, efficient data exchange across the ecosystem. Technical challenges including bandwidt

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Jiang, Wei, Jian-Hua Zhang, Xiao-Feng Cao, Bo Yang, and Wen-Tao Wang. "Fast data packet sorting method based on FPGA on-chip RAM." Journal of Instrumentation 20, no. 02 (2025): P02023. https://doi.org/10.1088/1748-0221/20/02/p02023.

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Abstract The study of fast sorting algorithms has long been an enduring research focus. Traditional sorting algorithms often suffer from high time complexity, typically staying at (n 2) or O(n × log2 n). Given the parallel processing advantages of field programmable gate array (FPGA), it has become a popular platform for algorithm acceleration. However, existing hardware sorting acceleration methods remain rooted in classical software algorithm models, merely leveraging hardware for parallel execution, without fully exploring the unique architecture and resources of FPGAs. In response, this pa

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Crosetto, M., N. Devanthéry, M. Cuevas-González, O. Monserrat, and B. Crippa. "Exploitation of the full potential of Persistent Scatterer Interferometry data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 75–78. http://dx.doi.org/10.5194/isprsarchives-xl-7-75-2014.

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The potential of Persistent Scatterer Interferometry (PSI) for deformation monitoring has been increasing in the last years and it will continue to do so in the short future, especially with the advent of the Sentinel-1 mission. The full exploitation of this potential requires two important components. The first one is the improvement of the PSI processing tools, to achieve massive and systematic data processing capabilities. The second one is the need to increase the capabilities to correctly analyze and interpret the PSI results. The paper addresses both components. The key features of the P

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Leja, Laura, Vitālijs Purlans, Rihards Novickis, Andrejs Cvetkovs, and Kaspars Ozols. "Mathematical Model and Synthetic Data Generation for Infra-Red Sensors." Sensors 22, no. 23 (2022): 9458. http://dx.doi.org/10.3390/s22239458.

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A key challenge in further improving infrared (IR) sensor capabilities is the development of efficient data pre-processing algorithms. This paper addresses this challenge by providing a mathematical model and synthetic data generation framework for an uncooled IR sensor. The developed model is capable of generating synthetic data for the design of data pre-processing algorithms of uncooled IR sensors. The mathematical model accounts for the physical characteristics of the focal plane array, bolometer readout, optics and the environment. The framework permits the sensor simulation with a range

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Shkirdov, D. A., E. S. Sagatov, and P. S. Dmitrenko. "Trap method in ensuring data security." Information Technology and Nanotechnology, no. 2416 (2019): 189–98. http://dx.doi.org/10.18287/1613-0073-2019-2416-189-198.

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This paper presents the results of data analysis from a geographically distributed honeypot network. Such honeypot servers were deployed in Samara, Rostov on Don, Crimea and the USA two years ago. Methods for processing statistics are discussed in detail for secure remote access SSH. Lists of attacking addresses are highlighted, and their geographical affiliation is determined. Rank distributions were used as the basis for statistical analysis. The intensity of requests to each of the 10 installed services was then calculated.

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Sarah, L. Johnson. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 01, no. 02 (2025): 31–41. https://doi.org/10.5281/zenodo.15147384.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behi

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Ravi, Shankar Koppula. "Real-world Use Cases of Databricks in Big Data Projects." Journal of Scientific and Engineering Research 8, no. 12 (2021): 253–63. https://doi.org/10.5281/zenodo.12798308.

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As data becomes a critical asset for businesses, the need for efficient and scalable data processing frameworks has never been greater. Apache Spark, a powerful big data technology, addresses these demands with its user-friendly interface and rapid performance. Databricks, a managed service built on Apache Spark, further enhances this capability by offering a cloud-based platform that streamlines the development and deployment of data products. This paper explores several real-world use cases of Databricks in large-scale data projects, highlighting its role in data ingestion, transformation, e

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Researcher. "DATA ENGINEERING FOR REAL-TIME ANALYTICS: TURNING DATA INTO INSTANT INSIGHTS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 752–63. https://doi.org/10.5281/zenodo.13889767.

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This article explores the transformative impact of real-time analytics across industries driven by advances in data engineering. It examines the key components of real-time analytics systems, including data ingestion, stream processing, in-memory storage, and visualization tools. The article demonstrates how real-time analytics enables personalized user experiences, optimized operations, and data-driven decision-making through case studies of American subscription video streaming services and American multinational retail corporations. The benefits of rapid insights, improved customer experien

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Suárez-Paniagua, Víctor, Arlene Casey, Charis A. Marwick, et al. "Care home resident identification: A comparison of address matching methods with Natural Language Processing." PLOS ONE 19, no. 12 (2024): e0309341. https://doi.org/10.1371/journal.pone.0309341.

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Background Care home residents are a highly vulnerable group, but identifying care home residents in routine data is challenging. This study aimed to develop and validate Natural Language Processing (NLP) methods to identify care home residents from primary care address records. Methods The proposed system applies an NLP sequential filtering and preprocessing of text, then the calculation of similarity scores between general practice (GP) addresses and care home registered addresses. Performance was evaluated in a diagnostic test study comparing NLP prediction to independent, gold-standard man

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Sambu, Patach Arrojula. "Efficient Event Grouping Algorithm for Mobile Analytics: Reducing Data Footprint." European Journal of Advances in Engineering and Technology 10, no. 1 (2023): 124–29. https://doi.org/10.5281/zenodo.13919609.

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The rapid growth in mobile applications has led to an exponential increase in the number of events generated and transmitted for analytics purposes. This surge in data volume significantly impacts data costs and performance for clients, while also demanding more resources for backend processing. In this paper, we propose an innovative approach to mitigate these challenges by reducing the footprint of events through efficient grouping and redundancy elimination. Our method involves aggregating related events into predefined buckets and optimizing storage by removing redundant data, retaining on

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Gururaj Thite. "Building Expertise in Data Engineering for AI Applications: A Comprehensive Guide." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 01–07. https://doi.org/10.32996/jcsts.2025.7.3.1.

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Data engineering has evolved significantly with the integration of artificial intelligence in the financial sector, demanding robust infrastructures and sophisticated practices. This comprehensive guide explores the essential competencies, tools, and best practices required for modern data engineers to excel in AI-driven financial systems. It details the transformation from traditional batch processing to real-time streaming architectures, examining distributed computing solutions, cloud-native implementations, and quality assurance frameworks. The guide addresses critical aspects of system ar

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Deepak Chanda. "Optimizing AI and Robotics-driven Automation Systems: The Synergy of Data Engineering and Data Science in Scalable Intelligent Automation." Journal of Electrical Systems 21, no. 1s (2025): 126–31. https://doi.org/10.52783/jes.8360.

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The intersection of data engineering and artificial intelligence (AI) has revolutionized modern industries using scalable, efficient, and intelligent automation. AI applications rely on robust data engineering frameworks for data ingestion, processing, and storage to feed high-quality inputs to machine learning algorithms. This paper explores the symbiosis between AI and data engineering in terms of automation, robotics, scalability, and real-time analytics. Data integration, governance, and performance optimization issues are considered, along with AI-driven solutions that streamline data wor

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Raghavendra Kurva. "Real-Time Data Integrity Validation Using Blockchain for Autonomous Vehicles." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1275–82. https://doi.org/10.32628/cseit25112455.

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This article presents a comprehensive framework for implementing blockchain-based data integrity validation in autonomous vehicles. The proposed system addresses critical challenges in securing real-time sensor data through a hybrid architecture combining Hyperledger Fabric with Apache Kafka. By integrating distributed ledger technology with optimized data processing mechanisms, the system achieves both security and performance requirements essential for autonomous vehicle operations. The architecture incorporates smart contracts for data validation, multi-layered security protocols, and effic

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Jaydeep Taralkar. "Designing scalable financial data pipelines with cloudera." Global Journal of Engineering and Technology Advances 23, no. 1 (2025): 420–26. https://doi.org/10.30574/gjeta.2025.23.1.0102.

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This technical article explores the design and implementation of scalable financial data pipelines using Cloudera's ecosystem. It examines the unique challenges facing financial institutions in managing massive volumes of diverse data types for applications including high-frequency trading, risk assessment, and regulatory compliance. The article details how Cloudera's integrated platform of open-source technologies—including Hadoop, Spark, Kafka, and specialized components—addresses these challenges through a comprehensive architectural paradigm. The article presents evidence-based performance

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J.J.Jayakanth. "Dynamic Object Detection in Surveillance Videos using Temporal Convolutional Networks and Federated Learning in Edge Computing Environments." Journal of Electrical Systems 20, no. 5s (2024): 2009–15. http://dx.doi.org/10.52783/jes.2537.

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This research addresses the importance of advancing dynamic object detection in surveillance videos by introducing a novel framework that integrates Temporal Convolutional Networks (TCNs) and Federated Learning (FL) within edge computing environments. This research is motivated by the critical need for real-time threat response, enhanced security measures, and privacy preservation in dynamic surveillance scenarios. Leveraging TCNs, the system captures temporal dependencies, providing a comprehensive understanding of object movements. FL ensures decentralized model training, mitigating privacy

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Martyniuk, T. B., and D. O. Katashynskyi. "Peculiarities of associative data processing in intelligent systems." Optoelectronic Information-Power Technologies 49, no. 1 (2025): 44–52. https://doi.org/10.31649/1681-7893-2025-49-1-44-52.

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Associative operations are computational massively parallel procedures over large data sets. This explains their widespread use in such application areas as database management systems (DBMS), searching and sorting IP addresses in computer networks, and ranking data, for example, in decision-making subsystems as part of intelligent systems, in particular, for medical diagnostics. This is due, not least, to the fact that associative operations include selection by foreign key, searching for data by analogy, sorting and ranking of elements of a data set. This paper presents the results of an ana

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Bhumeka, Narra Dheeraj Varun Kumar Reddy Buddula Hari Hara Sudheer Patchipulusu Achuthananda Reddy Polu Navya Vattikonda and Anuj Kumar Gupta. "Advanced Edge Computing Frameworks for Optimizing Data Processing and Latency in IoT Networks." JOETSR-Journal of Emerging Trends in Scientific Research 01, no. 01 (2023): 1–10. https://doi.org/10.5281/zenodo.15335462.

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The advent of edge computing has been revolutionary in improving data processing efficiency andreducing latency in IoT networks. By decentralizing computational tasks, edge computing enables real-timeanalytics and scalability while reducing reliance on centralized cloud infrastructures. This paper exploresadvanced frameworks, including hierarchical and decentralized architectures, that integrate AI and machinelearning to enhance predictive optimization and resource management. It also addresses challenges such asprotocol compatibility, energy efficiency, and operational complexities. The revie

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Moore, Philip T., and Hai V. Pham. "Personalization and rule strategies in data-intensive intelligent context-aware systems." Knowledge Engineering Review 30, no. 2 (2015): 140–56. http://dx.doi.org/10.1017/s0269888914000265.

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AbstractThe concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been

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Fasihuddin, Mirza. "Architecting scalable and reliable data ingestion pipelines to efficiently ingest large volumes of data into Hadoop clusters." European Journal of Advances in Engineering and Technology 9, no. 3 (2022): 153–58. https://doi.org/10.5281/zenodo.11213915.

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The rapid growth in data generation, fueled by trends like IoT proliferation and digital transformation, highlights the urgent need for scalable and reliable data ingestion pipelines to manage vast datasets within Hadoop clusters. This paper addresses the challenges of designing such pipelines, focusing on architecture, scalability, and reliability. It explores strategies for implementing resilient pipelines, considering fault tolerance, data consistency, and adaptability to evolving data sources and business needs. By comprehensively addressing these challenges, organizations can optimize dat

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More, Arjit Amol. "Natural Language Processing - Based Structured Data Extraction from Unstructured Clinical Notes." Journal of Contemporary Medical Practice 6, no. 8 (2024): 327–30. http://dx.doi.org/10.53469/jcmp.2024.06(08).67.

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Electronic Health Records (EHRs) are pivotal in modern healthcare, housing a treasure trove of patient information. They are real-time, patient-centered records that make information available instantly and securely to authorized users. However, a substantial portion of this data resides in unstructured clinical notes, presenting significant challenges for data extraction and utilization. This research paper investigates the issues posed by unstructured clinical notes application of Natural Language Processing (NLP) techniques in the healthcare sector to extract structured patient data from un

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Comandè, Giovanni, and Giulia Schneider. "Regulatory Challenges of Data Mining Practices: The Case of the Never-ending Lifecycles of ‘Health Data’." European Journal of Health Law 25, no. 3 (2018): 284–307. http://dx.doi.org/10.1163/15718093-12520368.

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Abstract Health data are the most special of the ‘special categories’ of data under Art. 9 of the General Data Protection Regulation (GDPR). The same Art. 9 GDPR prohibits, with broad exceptions, the processing of ‘data concerning health’. Our thesis is that, through data mining technologies, health data have progressively undergone a process of distancing from the healthcare sphere as far as the generation, the processing and the uses are concerned. The case study aims thus to test the endurance of the ‘special category’ of health data in the face of data mining technologies and the never-end

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Adhwaryu, Himanshu. "Real-Time Data Ecosystems in Insurance: A Comprehensive Analysis of Claims Processing and Policy Management Transformation." International Journal of Advances in Engineering and Management 7, no. 4 (2025): 286–93. https://doi.org/10.35629/5252-0704286293.

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Real-time data ecosystems are transforming the property and casualty (P&C) insurance industry through enhanced claims processing, policy management, and customer service capabilities. This transformation encompasses advanced analytics, artificial intelligence, and cloud-native architectures that enable insurers to process and analyze vast amounts of data instantaneously. The implementation of these systems addresses traditional challenges, such as data silos and batch processing limitations, while enabling sophisticated fraud detection and dynamic risk assessment. Through Apache Kafka and

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