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Journal articles on the topic 'Real-time data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Martha, Ranjith. "Real-Time Data Ingestion for Big Data Processing." International Journal of Science and Research (IJSR) 14, no. 2 (2025): 570–72. https://doi.org/10.21275/sr25209075243.

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Seenivasan, Dhamotharan. "Real-Time Data Processing with Streaming ETL." International Journal of Science and Research (IJSR) 12, no. 11 (2023): 2185–92. https://doi.org/10.21275/sr24619000026.

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Patrick Bell, Denis, Eliasu Tambominyi, and Yang Chunting. "Real-Time Stream Processing of Big Data." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 1247–52. https://doi.org/10.21275/sr21320045639.

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Karan, Patel, Sakaria Yash, and Bhadane Chetashri. "Real Time Data Processing Frameworks." International Journal of Data Mining & Knowledge Management Process (IJDKP) 5, no. 5 (2019): 49–63. https://doi.org/10.5281/zenodo.3406010.

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On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being

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Vennamaneni, Pradeep Rao. "Real-Time Financial Data Processing Using Apache Spark and Kafka." International journal of data science and machine learning 05, no. 01 (2025): 137–69. https://doi.org/10.55640/ijdsml-05-01-16.

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The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement. They are made possible through these open-source platforms that publicly ingest, process, and make decisions. Integrating cloud-native infrastructure—usi

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Patel, Karan, Yash Sakaria, and Chetashri Bhadane. "Real Time Data Processing Framework." International Journal of Data Mining & Knowledge Management Process 5, no. 5 (2015): 49–63. http://dx.doi.org/10.5121/ijdkp.2015.5504.

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Achanta, Mounica. "The Impact of Real - Time Data Processing on Business Decision - making." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 400–404. http://dx.doi.org/10.21275/sr24708033511.

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K Singhal, Dhruv. "Real-Time Data Processing and Analysis in MIS: Challenges and Solutions." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1295–98. http://dx.doi.org/10.21275/sr24415195628.

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Benický, Peter, and Ladislav Jurišica. "Real Time Motion Data Preprocessing." Journal of Electrical Engineering 61, no. 4 (2010): 247–51. http://dx.doi.org/10.2478/v10187-010-0035-2.

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Real Time Motion Data PreprocessingThere is a lot of redundant data for image processing in an image, in motion picture as well. The more data for image processing we have, the more time is needed for preprocessing it. That is why we need to work with important data only. In order to identify or classify motion, data processing in real time is needed.

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Matai, Puneet, and Abir Bhatia. "Architecting for Real - Time Analytics: Leveraging Stream Processing and Data Warehousing Integration." International Journal of Science and Research (IJSR) 13, no. 9 (2024): 1586–90. http://dx.doi.org/10.21275/sr24925170923.

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Mehendale, Pushkar. "Survey on Real-Time Data Processing in Finance Using Machine Learning Techniques." International Journal of Science and Research (IJSR) 8, no. 7 (2019): 1910–13. http://dx.doi.org/10.21275/sr24810081140.

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MOMTSELIDZE, Nodar, and Ana TSITSAGI. "Apache Kafka - Real-time Data Processing." Journal of Technical Science and Technologies 4, no. 2 (2016): 31–34. http://dx.doi.org/10.31578/jtst.v4i2.80.

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Apache Kafka is creating a lot of buzz these days. While LinkedIn, where Kafka was founded, is the most well known user, there are many companies that use this technology successfully. Kafka has several features that make it a good t for companies' requirements: scalability, data partitioning, low latency, and the ability to handle large number of diverse consumers. It works with Apache Storm and Apache Spark for real-time analysis and rendering of streaming data. The combination of messaging and processing technologies enables stream processing at linear scale. Common use cases include: Mess

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Taylor, S., and R. Taylor. "Parallel processing and real-time data acquisition." IEEE Transactions on Nuclear Science 37, no. 2 (1990): 355–60. http://dx.doi.org/10.1109/23.106644.

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14

Safaei, Ali A. "Real-time processing of streaming big data." Real-Time Systems 53, no. 1 (2016): 1–44. http://dx.doi.org/10.1007/s11241-016-9257-0.

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Koppichetti, Ravi Kiran. "Real-Time Data Processing for Retail Insights." International Journal of Multidisciplinary Research and Growth Evaluation. 5, no. 4 (2024): 1378–86. https://doi.org/10.54660/.ijmrge.2024.5.4.1378-1386.

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Real-time data is revolutionizing the retail industry by enabling businesses to analyze and respond to data as it is generated. This capability fundamentally alters how retailers operate, interact with customers, and make strategic decisions. From personalized marketing and dynamic pricing to inventory optimization and fraud detection, real-time data processing empowers retailers to enhance customer experiences, streamline operations, and sustain a competitive advantage. This paper examines the significance of real-time data processing in retail, highlighting its principal applications, advant

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Santhosh Kumar Rai. "Real-Time Data Processing with Cloud Technologies." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 927–43. https://doi.org/10.32628/cseit25112422.

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Real-time data processing, empowered by cloud technologies, has revolutionized how businesses transform raw data streams into actionable insights. This transformative approach enables organizations to respond instantly to changing conditions, identify emerging opportunities and threats, and make decisions based on current information. The evolution from traditional batch processing to modern stream processing architectures represents a technical advancement and a fundamental shift in how enterprises conceptualize their relationship with data. By examining key components, implementation challen

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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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18

Mutasher, Watheq Ghanim, and Abbas Fadhil Aljuboori. "Real Time Big Data Sentiment Analysis and Classification of Facebook." Webology 19, no. 1 (2022): 1112–27. http://dx.doi.org/10.14704/web/v19i1/web19076.

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Many peoples use Facebook to connect and share their views on various issues, with the majority of user-generated content consisting of textual information. Since there is so much actual data from people who are posting messages on their situation in real time thoughts on a range of subjects in everyday life, the collection and analysis of these data, which may well be helpful for political decision or public opinion monitoring, is a worthwhile research project. Therefore, in this paper doing to analyze for public text post on Facebook stream in real time through environment Hadoop ecosystem b

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19

Sampath Kini K. "Exploring Real-Time Data Processing Using Big Data Frameworks." Communications on Applied Nonlinear Analysis 31, no. 8s (2024): 620–34. http://dx.doi.org/10.52783/cana.v31.1561.

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Big data frameworks that weaken the throughput of data processing, allowing for real-time data processing like Apache Spark, Kafka, and Flink are other developments. Regarding quick decisions by each measurement, the scalability, fault tolerance, and latency of three architectures Here each stream processing, lambda, and Kappa have been further studied and measured to approach a conclusion. Based on a methodical survey of literature, performance laws, and case studies, all three frameworks and architectures pros and cons measure us, which can then be used for separate operations use situations

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Anju Santosh Yedatkar. "Real-time data analytics in distributed systems." International Journal of Scientific Research in Modern Science and Technology 3, no. 6 (2024): 09–16. http://dx.doi.org/10.59828/ijsrmst.v3i6.215.

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Real-time data analytics involves the processing and analysis of data as it arrives, delivering immediate insights that are crucial for time-sensitive applications. This research explores the platforms and techniques necessary for supporting real-time analytics, extending beyond traditional Event Processing Systems (EPS) to include broader big data contexts that integrate both 'data at rest' and 'data in motion' solutions. A detailed case study is presented, showcasing the application of the Event Swarm complex event processing engine in addressing financial analytics challenges. The study ide

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21

Healey, Christopher G., Kellogg S. Booth, and James T. Enns. "Visualizing real-time multivariate data using preattentive processing." ACM Transactions on Modeling and Computer Simulation 5, no. 3 (1995): 190–221. http://dx.doi.org/10.1145/217853.217855.

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22

Alfian, Ganjar, Muhammad Fazal Ijaz, Muhammad Syafrudin, M. Alex Syaekhoni, Norma Latif Fitriyani, and Jongtae Rhee. "Customer behavior analysis using real-time data processing." Asia Pacific Journal of Marketing and Logistics 31, no. 1 (2019): 265–90. http://dx.doi.org/10.1108/apjml-03-2018-0088.

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PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in dif

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23

Miller, Ben, and Stephen Mick. "Real-Time Data Processing using Python in DigitalMicrograph." Microscopy and Microanalysis 25, S2 (2019): 234–35. http://dx.doi.org/10.1017/s1431927619001909.

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24

Corin, William J., David T. George, Joanne Y. Reilley, and William P. Santamore. "Virtual real-time digital processing of hemodynamic data." Catheterization and Cardiovascular Diagnosis 26, no. 1 (1992): 1–7. http://dx.doi.org/10.1002/ccd.1810260102.

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25

Sasmal, Shubhodip. "Real-time Data Processing with Machine Learning Algorithms." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 11, no. 4 (2023): 91–96. http://dx.doi.org/10.55083/irjeas.2023.v11i04012.

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In the era of information abundance, organizations are faced with the challenge of harnessing real-time data streams to extract valuable insights swiftly. This research paper explores the intersection of real-time data processing and machine learning algorithms, aiming to develop a comprehensive understanding of their integration for efficient decision-making in dynamic environments. The paper begins by delineating the landscape of real-time data processing, emphasizing the significance of timely and accurate information in contemporary business scenarios. It delves into the challenges posed b

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26

Terauchi, Atsushi, Kenichi Ooto, Noriyuki Takahashi, Kei Harada, and Ikuo Yamasaki. "Data Exchange Technology Providing Real-time Data Processing and Scalability." NTT Technical Review 15, no. 9 (2017): 19–25. http://dx.doi.org/10.53829/ntr201709fa4.

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27

Hassan, Alaa Abdelraheem, and Tarig Mohammed Hassan. "Real-Time Big Data Analytics for Data Stream Challenges: An Overview." European Journal of Information Technologies and Computer Science 2, no. 4 (2022): 1–6. http://dx.doi.org/10.24018/compute.2022.2.4.62.

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The conventional approach of evaluating massive data is inappropriate for real-time analysis; therefore, analysing big data in a data stream remains a critical issue for numerous applications. It is critical in real-time big data analytics to process data at the point where they are arriving at a quick reaction and good decision making, necessitating the development of a novel architecture that allows for real-time processing at high speed and low latency. Processing and anlayzing a data stream in real-time is critical for a variety of applications; however, handling a large amount of data fro

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28

Peter, Safir. "REAL-TIME VIDEO PROCESSING WITH FPGAS." Annali d'Italia 39 (January 25, 2023): 85–88. https://doi.org/10.5281/zenodo.7568965.

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In this article, I would like to talk about real-time video streaming implementation on FPGA and how to correctly build the structure of the whole project. How to choose the right color space. How to convert from one color space to another? How to use shift registers to speed up an algorithm on an FPGA? How to build a memory buffer for real time video processing and why to use an FIFO buffer instead of writing the data directly into the SDRAM and why it is good using the different filters and which filters are better to use and when.

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29

Sanjay Lote, Praveena K B, and Durugappa Patrer. "Real-time data stream processing in large-scale systems." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 560–70. https://doi.org/10.30574/wjarr.2022.15.3.0903.

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Real-time data stream processing has emerged as a crucial element in modern large-scale systems, facilitating rapid decision-making and real-time analytics across various domains. As data volumes continue to grow exponentially, the need for efficient, scalable, and fault-tolerant stream processing solutions has become more pressing. This paper provides a comprehensive exploration of real-time data processing architectures, highlighting key components such as distributed stream processing frameworks, parallel data pipelines, and event-driven computing models. The study delves into state-of-the-

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Weifeng Shan, Weifeng Shan, Jianqiao Li Weifeng Shan, Yuntian Teng Jianqiao Li, Huiling Chen Yuntian Teng, Zhiyang Li Huiling Chen, and Maofa Wang Zhiyang Li. "A Progressive Real-time Visualization Method for Earthquake Big Data." 電腦學刊 33, no. 1 (2022): 087–100. http://dx.doi.org/10.53106/199115992022023301009.

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<p>As the volume of seismic observation time-series data grows larger, web-based visualization schemes suffer from longer system response times. Although big data visualization schemes based on sampling and filtering can greatly reduce the data scale and shorten transmission time, what it gains in speed it loses in information. Progressive visualization has become an increasingly popular scheme because it can quickly &ldquo;see&rdquo; some results without having to wait for all the data, thus enabling users to grasp a data-change trend quickly and perceive the rules behind it. In

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Hari, Prasad Bomma. "Real-Time Data Streaming and Processing using Synapse Analytics." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 12, no. 6 (2024): 1–5. https://doi.org/10.5281/zenodo.14762564.

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Extract, Transform, Load (ETL) is a traditional method widely used for data integration, involving extracting data from various sources, transforming it to meet operational needs, and loading it into a target data warehouse. Regular ETL processes typically scheduled at intervals like daily or weekly, offer advantages such as simplifying data processing and reducing resource usage during off peak hours. However, they also present significant drawbacks, including latency and difficulty in scaling with large data volumes, which can lead to processing delays and potential system failures. The pape

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Madhuranthakam, Reddy Srikanth. "Scalable Data Engineering Pipelines for Real-Time Analytics in Big Data Environments." FMDB Transactions on Sustainable Computing Systems 2, no. 3 (2024): 154–66. https://doi.org/10.69888/ftscs.2024.000262.

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With the world becoming increasingly data-driven, actionable insights and decisions that depend on real-time analytics have been at the forefront. However, processing huge volumes of data in real time requires very strong, scalable, and efficient data engineering pipelines. This paper describes the design, development, and optimization of scalable data engineering pipelines for real-time analytics in big data environments. Ingestion, processing, storage and visualization, along with their interactions within the distributed computing setup, will all be part of the paper. More best practices wi

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Sangeeta Rani. "Tools and techniques for real-time data processing: A review." International Journal of Science and Research Archive 14, no. 1 (2025): 1872–81. https://doi.org/10.30574/ijsra.2025.14.1.0252.

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Real-time data processing is an essential component in the modern data landscape, where vast amounts of data are generated continuously from various sources such as Internet of Things devices, social media, financial transactions, and manufacturing systems. Unlike traditional batch processing methods that analyse data in intervals, real-time data processing enables the continuous intake, manipulation, and analysis of data within milliseconds of generation. This capability is critical for applications requiring instant insights and rapid decision-making, including fraud detection, predictive ma

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34

Joshi, Abhijit. "Decentralized Edge Computing Paradigms: Architecting Low - Latency, Real - Time Data Processing Frameworks in the IoT Era." International Journal of Science and Research (IJSR) 10, no. 11 (2021): 1531–38. http://dx.doi.org/10.21275/sr24608150129.

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35

Buthukuri, Bhavani, and Sivaram Rajeyyagari. "Investigation on Processing of Real-Time Streaming Big Data." International Journal of Engineering & Technology 7, no. 3.13 (2018): 79. http://dx.doi.org/10.14419/ijet.v7i3.13.16329.

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MapReduce is the most widely used for huge data processing and it is a part of the Hadoop big data and this will provide the quality and efficient results because of their processing functions. For the batch jobs, Hadoop is the proper and also there is inflated request for non-batch elements homogeneous interactive jobs, and high data currents. For this non-batch assignments, consider Hadoop is not useful and present situations are recommending to these new crises. In this paper, these are divided into two stages that are real-time processing, and stream processing of big data. For every stage

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Goto, Hiroyuki. "Time-series data server optimized for multichannel and real-time processing." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 89, no. 7 (2006): 8–18. http://dx.doi.org/10.1002/ecjc.20257.

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37

S.C, Prof Cholke. "REAL TIME DATA RETRIEVAL AND CONCURRENT DATA FLOW." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34047.

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Real -Time analytics (RTA) has emerged as a distinct branch of big data analytics focusing on the velocity aspect of big data , in which data is prepared , processed and analyzed as it arrives, intending to generate insight and create business value in near real-time. The software system being produced is called E-Commerce Web. This system is designed to “Provide Real Time data Retrieval & Management” for the process of placing an order on the Internet and facilitating the actual delivery of the product. E-Commerce is now seen as a reality for many businesses and a normal part of a busines

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Naveen Kumar Pedada. "Advancements in Real-Time Data Processing in Medical Research." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1676–86. https://doi.org/10.32628/cseit25112734.

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Real-time data processing has revolutionized medical research by transforming how clinical investigations are conducted and analyzed. This article examines the evolution from traditional batch processing to instantaneous data analysis across the healthcare ecosystem. The article explores five key areas: the historical context and transformative potential of real-time processing; its application in clinical trials through electronic data capture and risk-based monitoring; artificial intelligence applications in drug discovery that have dramatically accelerated therapeutic development; breakthro

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Tóth, Tamás, and István Majzik. "Formal Verification of Real-Time Systems with Data Processing." Periodica Polytechnica Electrical Engineering and Computer Science 61, no. 2 (2017): 166. http://dx.doi.org/10.3311/ppee.9766.

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The behavior of practical safety critical systems often combines real-time behavior with structured data flow. To ensure correctness of such systems, both aspects have to be modeled and formally verified. Time related behavior can be efficiently modeled and analyzed in terms of timed automata. At the same time, program verification techniques like abstract interpretation and software model checking can efficiently handle data flow. In this paper, we describe a simple formalism that represents both aspects of such systems in a uniform and explicit way, thus enables the combination of formal ana

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40

Lee, Mo-se, Min-su Kang, Hong-joon Kim, and Jae-hun Kim. "Real-Time Data Processing Architecture for a Smart Cities." Journal of Korean Institute of Communications and Information Sciences 46, no. 2 (2021): 401–9. http://dx.doi.org/10.7840/kics.2021.46.2.401.

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SABZIEV, Elkhan. "ALGORITHM OF AIRCRAFT FLIGHT DATA PROCESSING IN REAL-TIME." Scientific Journal of Silesian University of Technology. Series Transport 108 (September 1, 2020): 213–21. http://dx.doi.org/10.20858/sjsutst.2020.108.17.

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Miller, Benjamin K., Bernhard Schaffer, Winnie Lei, and Cory Czarnik. "Extensible Real-Time Data Processing with Python in DigitalMicrograph." Microscopy and Microanalysis 28, S1 (2022): 128–29. http://dx.doi.org/10.1017/s1431927622001416.

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Wohlfeil, J., A. Börner, M. Buder, I. Ernst, D. Krutz, and R. Reulke. "REAL TIME DATA PROCESSING FOR OPTICAL REMOTE SENSING PAYLOADS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B5 (July 24, 2012): 63–68. http://dx.doi.org/10.5194/isprsarchives-xxxix-b5-63-2012.

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Shutler, J. D., T. J. Smyth, P. E. Land, and S. B. Groom. "A near‐real time automatic MODIS data processing system." International Journal of Remote Sensing 26, no. 5 (2005): 1049–55. http://dx.doi.org/10.1080/01431160412331299244.

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Laoreti, Stefano, Davide Renzi, Raffaele Parisi, and Aurelio Uncini. "Data Fusion Framework: concurrent architecture for real-time processing." International Journal of Information and Communication Technology 1, no. 3/4 (2008): 424. http://dx.doi.org/10.1504/ijict.2008.024013.

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Bonino, Dario, and Luigi De Russis. "Mastering real-time big data with stream processing chains." XRDS: Crossroads, The ACM Magazine for Students 19, no. 1 (2012): 83–86. http://dx.doi.org/10.1145/2331042.2331050.

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Hillman, Chris, Yasmeen Ahmad, Mark Whitehorn, and Andy Cobley. "Near Real-Time Processing of Proteomics Data Using Hadoop." Big Data 2, no. 1 (2014): 44–49. http://dx.doi.org/10.1089/big.2013.0036.

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Mohammed, Abdul Hameed. "REAL-TIME DATA PROCESSING IN CLOUD AND EDGE COMPUTING." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY 15, no. 6 (2024): 1940–51. https://doi.org/10.34218/ijcet_15_06_166.

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Naresh Babu Kilaru. "DESIGN REAL-TIME DATA PROCESSING SYSTEMS FOR AI APPLICATIONS." International Journal for Research Publication and Seminar 15, no. 3 (2024): 472–81. http://dx.doi.org/10.36676/jrps.v15.i3.1538.

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Online analytics systems are vital for ensuring the high efficiency of AI in response to real-time situations requiring agile decision-making. The present paper explores real-time data processing and topology, featuring the application of edge computing and cloud-based services and systems. Through simulation reports, the study shows how these systems handle significant data traffic and minimal delays in healthcare monitoring, automated transport systems, and smart homes. Possible data consistency, system growth, and redundancy issues are recognized, and recommendations are made to improve nav

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Naresh Babu Kilaru. "DESIGN REAL-TIME DATA PROCESSING SYSTEMS FOR AI APPLICATIONS." International Journal for Research Publication and Seminar 14, no. 5 (2023): 472–81. http://dx.doi.org/10.36676/jrps.v14.i5.1538.

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Online analytics systems are vital for ensuring the high efficiency of AI in response to real-time situations requiring agile decision-making. The present paper explores real-time data processing and topology, featuring the application of edge computing and cloud-based services and systems. Through simulation reports, the study shows how these systems handle significant data traffic and minimal delays in healthcare monitoring, automated transport systems, and smart homes. Possible data consistency, system growth, and redundancy issues are recognized, and recommendations are made to improve nav

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