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Articoli di riviste sul tema "Real-time data processing"

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Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 27 luglio 2024

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1

Patel, Karan, Yash Sakaria e Chetashri Bhadane. "Real Time Data Processing Framework". International Journal of Data Mining & Knowledge Management Process 5, n. 5 (30 settembre 2015): 49–63. http://dx.doi.org/10.5121/ijdkp.2015.5504.

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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, n. 4 (5 aprile 2024): 1295–98. http://dx.doi.org/10.21275/sr24415195628.

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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, n. 7 (5 luglio 2024): 400–404. http://dx.doi.org/10.21275/sr24708033511.

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MOMTSELIDZE, Nodar, e Ana TSITSAGI. "Apache Kafka - Real-time Data Processing". Journal of Technical Science and Technologies 4, n. 2 (22 maggio 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: Messaging, Website activity tracking, Log aggregation, Stream Processing, Commit log.

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Benický, Peter, e Ladislav Jurišica. "Real Time Motion Data Preprocessing". Journal of Electrical Engineering 61, n. 4 (1 luglio 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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Taylor, S., e R. Taylor. "Parallel processing and real-time data acquisition". IEEE Transactions on Nuclear Science 37, n. 2 (aprile 1990): 355–60. http://dx.doi.org/10.1109/23.106644.

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Safaei, Ali A. "Real-time processing of streaming big data". Real-Time Systems 53, n. 1 (1 agosto 2016): 1–44. http://dx.doi.org/10.1007/s11241-016-9257-0.

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Mutasher, Watheq Ghanim, e Abbas Fadhil Aljuboori. "Real Time Big Data Sentiment Analysis and Classification of Facebook". Webology 19, n. 1 (20 gennaio 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 by using apache spark with NLTK python. The post or feeds are gathered form the Facebook API in real time the data stored database used Apache spark to quick query processing the text partitions in each data nodes (machine). Also used Amazon cloud based Hadoop cluster ecosystem into processing of huge data and eliminate on-site hardware, IT support, and other operational difficulties and installation configuration Hadoop such as Hadoop distribution file system and Apache spark. By using the principle of decision dictionary, emotion analysis is used as positive, negative, or neutral and execution two algorithms in machine learning (naive bias & support vector machine) to build model predict the outcome demonstrates a high level of precision in sentiment analysis.

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Healey, Christopher G., Kellogg S. Booth e James T. Enns. "Visualizing real-time multivariate data using preattentive processing". ACM Transactions on Modeling and Computer Simulation 5, n. 3 (luglio 1995): 190–221. http://dx.doi.org/10.1145/217853.217855.

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Alfian, Ganjar, Muhammad Fazal Ijaz, Muhammad Syafrudin, M. Alex Syaekhoni, Norma Latif Fitriyani e Jongtae Rhee. "Customer behavior analysis using real-time data processing". Asia Pacific Journal of Marketing and Logistics 31, n. 1 (14 gennaio 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 different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

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

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Corin, William J., David T. George, Joanne Y. Reilley e William P. Santamore. "Virtual real-time digital processing of hemodynamic data". Catheterization and Cardiovascular Diagnosis 26, n. 1 (maggio 1992): 1–7. http://dx.doi.org/10.1002/ccd.1810260102.

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Sasmal, Shubhodip. "Real-time Data Processing with Machine Learning Algorithms". INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 11, n. 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 by the velocity and volume of data generated continuously, necessitating advanced processing mechanisms capable of handling data streams in real-time. As the focus shifts to machine learning algorithms, the research outlines the diverse range of algorithms suitable for real-time applications. From online learning methods to streaming algorithms, the exploration encompasses techniques tailored to adapt and evolve with incoming data. This section also addresses the trade-offs between accuracy and computational efficiency, crucial considerations in real-time processing environments. The core of the paper lies in the synthesis of real-time data processing and machine learning algorithms. It investigates how machine learning models can be seamlessly integrated into data processing pipelines to analyze and respond to streaming data instantaneously. Case studies and practical implementations exemplify instances where predictive analytics and anomaly detection algorithms contribute to real-time decision support. Ethical considerations and challenges related to the deployment of machine learning in real-time settings are also examined. The paper advocates for responsible and transparent use of algorithms, emphasizing the importance of mitigating biases and ensuring accountability in decision-making processes driven by machine learning insights. this research paper provides a roadmap for organizations seeking to harness the synergy between real-time data processing and machine learning. The insights gained from this exploration pave the way for advancements in adaptive decision-making systems, offering a competitive edge in industries where rapid response to evolving data is paramount.

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Terauchi, Atsushi, Kenichi Ooto, Noriyuki Takahashi, Kei Harada e Ikuo Yamasaki. "Data Exchange Technology Providing Real-time Data Processing and Scalability". NTT Technical Review 15, n. 9 (settembre 2017): 19–25. http://dx.doi.org/10.53829/ntr201709fa4.

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Weifeng Shan, Weifeng Shan, Jianqiao Li Weifeng Shan, Yuntian Teng Jianqiao Li, Huiling Chen Yuntian Teng, Zhiyang Li Huiling Chen e Maofa Wang Zhiyang Li. "A Progressive Real-time Visualization Method for Earthquake Big Data". 電腦學刊 33, n. 1 (febbraio 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 “see” 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 this paper, a Cloudberry-based progressive real-time visualization schema for earthquake big data (PVSEBD) is proposed for the first time. It greatly shortens the transmission time of each data slice, improves the user interaction experience, and meets the long-term, large-scale visualization needs of earthquake consultation business. Because the correctness of average aggregation function (AVG) in progressive visualization is often not guaranteed, this paper proposes an innovative AVG translation rule solution based on the accumulability of the COUNT and SUM aggregation functions. The experimental results showed that PVSEBD automatically adjusts the amount of data returned each time according to size and has a shorter response time for each interaction compared with the solution based on the web-based visualization toolkit Portable Progressive Parallel Processing Pipeline (P5).</p> <p> </p>

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Hassan, Alaa Abdelraheem, e Tarig Mohammed Hassan. "Real-Time Big Data Analytics for Data Stream Challenges: An Overview". European Journal of Information Technologies and Computer Science 2, n. 4 (25 luglio 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 from a variety of sources, such as sensor networks, web traffic, social media, video streams, and other sources, is a considerable difficulty. The main goal of this paper is to give an overview of the current architecture for real time big data analytics, real-time data stream processing methods available, including their system architectures Lambda, kappa, and delta large data stream processing.

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S.C, Prof Cholke. "REAL TIME DATA RETRIEVAL AND CONCURRENT DATA FLOW". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n. 05 (16 maggio 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 business plan. Different key technologies used and highlights the significant features including real time data update via php, Inventory Management and Security, Employee Management and Attendance Functionality, User Registration and related issues, Payment options, Mobile Functionalities are considered. We proposed a system here consumer moves through the internet to the web site. From there, he decides that he wants to purchase something, so he is moved to the online transaction server, where all of the information he gives is encrypted. Once he has placed his order, the information moves through a private gateway to a Processing Network, where the issuing and acquiring banks complete or deny the transaction. This generally takes place in no more than 5-7seconds. It provides a brief overview of what e-commerce website business is about , its key features and the goals it aims to achieve. Key Words: Web Development, Technologies, E-Commerce, Real-Time Analytics, data streaming, Big data analytics, Data streaming, complex event processing.

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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, n. 7 (2006): 8–18. http://dx.doi.org/10.1002/ecjc.20257.

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Tóth, Tamás, e István Majzik. "Formal Verification of Real-Time Systems with Data Processing". Periodica Polytechnica Electrical Engineering and Computer Science 61, n. 2 (23 maggio 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 analysis methods for real-time systems and software using standard techniques.

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Lee, Mo-se, Min-su Kang, Hong-joon Kim e Jae-hun Kim. "Real-Time Data Processing Architecture for a Smart Cities". Journal of Korean Institute of Communications and Information Sciences 46, n. 2 (28 febbraio 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 (1 settembre 2020): 213–21. http://dx.doi.org/10.20858/sjsutst.2020.108.17.

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Miller, Benjamin K., Bernhard Schaffer, Winnie Lei e Cory Czarnik. "Extensible Real-Time Data Processing with Python in DigitalMicrograph". Microscopy and Microanalysis 28, S1 (22 luglio 2022): 128–29. http://dx.doi.org/10.1017/s1431927622001416.

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Wohlfeil, J., A. Börner, M. Buder, I. Ernst, D. Krutz e 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 (24 luglio 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 e S. B. Groom. "A near‐real time automatic MODIS data processing system". International Journal of Remote Sensing 26, n. 5 (marzo 2005): 1049–55. http://dx.doi.org/10.1080/01431160412331299244.

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Laoreti, Stefano, Davide Renzi, Raffaele Parisi e Aurelio Uncini. "Data Fusion Framework: concurrent architecture for real-time processing". International Journal of Information and Communication Technology 1, n. 3/4 (2008): 424. http://dx.doi.org/10.1504/ijict.2008.024013.

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Bonino, Dario, e Luigi De Russis. "Mastering real-time big data with stream processing chains". XRDS: Crossroads, The ACM Magazine for Students 19, n. 1 (settembre 2012): 83–86. http://dx.doi.org/10.1145/2331042.2331050.

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Hillman, Chris, Yasmeen Ahmad, Mark Whitehorn e Andy Cobley. "Near Real-Time Processing of Proteomics Data Using Hadoop". Big Data 2, n. 1 (marzo 2014): 44–49. http://dx.doi.org/10.1089/big.2013.0036.

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Zhou, Chunyang, Guohui Li, Jianjun Li e Bing Guo. "Energy-Aware Real-Time Data Processing for IoT Systems". IEEE Access 7 (2019): 171776–89. http://dx.doi.org/10.1109/access.2019.2956061.

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Sakata, S., M. Koiwa, T. Aoyagi e T. Matsuda. "Real time processor in JT-60 data processing system". Fusion Engineering and Design 48, n. 1-2 (agosto 2000): 225–30. http://dx.doi.org/10.1016/s0920-3796(00)00134-4.

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Stoew, B., P. Jarlemark, J. Johansson e G. Elgered. "Real-time processing of GPS data delivered by SWEPOS". Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 26, n. 6-8 (gennaio 2001): 493–96. http://dx.doi.org/10.1016/s1464-1895(01)00090-4.

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Benesch, Manfred, Hellmuth Kubin e Klaus Kabitzsch. "Processing of Real-time Data in Big Manufacturing Systems". Procedia Manufacturing 11 (2017): 2114–22. http://dx.doi.org/10.1016/j.promfg.2017.07.340.

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Watmuff, Jonathan H. "High-speed real-time processing of cross-wire data". Experimental Thermal and Fluid Science 10, n. 1 (gennaio 1995): 74–85. http://dx.doi.org/10.1016/0894-1777(94)00063-e.

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Lee, Sang-Young. "Integrated Processes of SDR Data for Real-time Processing". International Journal of Database Theory and Application 10, n. 7 (31 luglio 2017): 55–64. http://dx.doi.org/10.14257/ijdta.2017.10.7.05.

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Pourramezan, Reza, Houshang Karimi, Jean Mahseredjian e Mario Paolone. "Real-Time Processing and Quality Improvement of Synchrophasor Data". IEEE Transactions on Smart Grid 11, n. 4 (luglio 2020): 3313–24. http://dx.doi.org/10.1109/tsg.2020.2968814.

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Buthukuri, Bhavani, e Sivaram Rajeyyagari. "Investigation on Processing of Real-Time Streaming Big Data". International Journal of Engineering & Technology 7, n. 3.13 (27 luglio 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, the models are deliberate, stability and diversity to Hadoop. For every group, we have provided the working systems and structures. For the creation of the new examples, some experiments are conducted to improve the new results belongs to available Hadoop-based solutions.

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Simon, P. "Q-Gene: processing quantitative real-time RT-PCR data". Bioinformatics 19, n. 11 (21 luglio 2003): 1439–40. http://dx.doi.org/10.1093/bioinformatics/btg157.

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Gong, Jie, e Carlos H. Caldas. "Data processing for real-time construction site spatial modeling". Automation in Construction 17, n. 5 (luglio 2008): 526–35. http://dx.doi.org/10.1016/j.autcon.2007.09.002.

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Jian-Fang Xue, Jian-Fang Xue, Qing-Chuan Liu Jian-Fang Xue, Xiao-Yang Zhang Qing-Chuan Liu, Rui Fan Xiao-Yang Zhang e Wei-Min Liu Rui Fan. "Research on Real Time Data Monitoring Method for Intelligent Factory Equipment Based on Ethernet Communication". 電腦學刊 35, n. 1 (febbraio 2024): 227–33. http://dx.doi.org/10.53106/199115992024023501018.

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<p>This article focuses on the production and processing of sleeve parts, using Ethernet as a data transmission medium to complete data collection and monitoring of typical intelligent factory processing workshops. Firstly, the layout design of the intelligent factory equipment layer, communication layer, and system application layer was completed. Then, unified management of standard and non-standard protocols was implemented for communication between different devices. Then, based on the sensors and sensor data used in each production process, a data collection scheme was designed, and finally, the data collection system was designed, Real time monitoring of data between various devices in the system can guide enterprise production.</p> <p> </p>

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Yang, Jun, Yan-dong Cao, Guang-cai Sun, Meng-dao Xing e Liang Guo. "GF-3 data real-time processing method based on multi-satellite distributed data processing system". Journal of Central South University 27, n. 3 (marzo 2020): 842–52. http://dx.doi.org/10.1007/s11771-020-4335-9.

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Eum, Junho, Eyassu Berhanu e Sangyoon Oh. "Unmanned Aircraft Platform Based Real-time LiDAR Data Processing Architecture for Real-time Detection Information". KIISE Transactions on Computing Practices 21, n. 12 (15 dicembre 2015): 745–50. http://dx.doi.org/10.5626/ktcp.2015.21.12.745.

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Kostov, Martin, e Kalinka Kaloyanova. "Real-time data integration in information systems using stream processing for medical data". Annual of Sofia University St. Kliment Ohridski. Faculty of Mathematics and Informatics 110 (12 novembre 2023): 101–10. http://dx.doi.org/10.60063/gsu.fmi.110.101-110.

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Real-time data processing in medical information systems is becoming harder with the increase in data volume. Stream processing is a popular approach for real-time data processing, which can process large volumes of data including medical in a scalable manner. In some cases, medical data may not be available in real time because of privacy and security concerns. In this paper, we will explore the use of stream processing with static medical data using streaming platforms, Kafka, and Apache Spark. We will demonstrate how these platforms can be used to work with static data in streams and discuss the benefits and limitations of the approach. We also present a case study to illustrate the effectiveness and performance.

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Sir, Prof Ravishankar, Prathamesh P. Bhamanage, Pratik S. Patil, Suraj Y. Ahire e Durgesh P. Jadhav. "Twitter Analysis On Real Time Data". International Journal for Research in Applied Science and Engineering Technology 10, n. 11 (30 novembre 2022): 1052–56. http://dx.doi.org/10.22214/ijraset.2022.47524.

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Abstract: Sentiment analysis is mainly concerned with identifying and classifying opinions or emotions that are expressed within a text. These days, sharing opinions and expressing emotions through social networking websites has become very common. This paper presents an idea of extracting sentiments out of the tweet and an approach towards classifying a tweet into positive, negative or neutral. This approach can be in many ways useful to any organization, who gets mentioned or tagged in a tweet. Generally the tweets being unstructured in format, first of all the tweet needs to be converted into the structured format. In this paper, tweets are resolved using pre-processing phase and access of tweets has been accomplished via libraries using Twitter API.. We provide additional comparisons and extract alternatives. exams, apprenticeships, etc., are compared to find higher overall performance, and several scoring criteria have been developed for different techniques.

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Peppin, William A., e Walter F. Nicks. "Real-Time Analog and Digital Data Acquisition Through CUSP". Seismological Research Letters 63, n. 2 (1 aprile 1992): 181–89. http://dx.doi.org/10.1785/gssrl.63.2.181.

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Abstract The University of Nevada Seismological Laboratory operates an array of 60 analog short-period and 10 three-component digital telemetered seismic stations, 90 data traces in all, in Nevada and eastern California. Formerly, the seismic data streams were recorded and processed on three separate computers running disparate software and writing incompatible data formats which made access to the digital data quite cumbersome. These systems were recently replaced by a single computer system, a MicroVAX II running VAX/VMS, together with Generic CUSP (Caltech -U.S.G.S. Seismic Processing System), a controlled software system from the U.S.G.S. in Menlo Park. Telemetered digital data are stored simultaneously in two ways, unique to this network. First, these digital data are brought asynchronously into the computer using a standard direct-memory access interface and recorded continuously on an Exabyte 8-mm helical-scan tapedrive. Second, the digital data are passed through a D to A converter and intermixed with the incoming analog data stream used for routine network processing. This analog data stream is then itself digitized and presented to the computer. In this way, calibrated digital waveforms are available in the routine data processing stream, now entirely comprised of digital waveforms, used to locate earthquakes. At the same time, this allows easy access to these data in research applications involving the processing of seismic waveforms.

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Son, Jae Gi, e Jung Guk Kim. "Squall: A Real-time Big Data Processing Framework based on TMO Model for Real-time Events and Micro-batch Processing". Journal of KIISE 44, n. 1 (15 gennaio 2017): 84–94. http://dx.doi.org/10.5626/jok.2017.44.1.84.

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Stensland, Håkon Kvale, Vamsidhar Reddy Gaddam, Marius Tennøe, Espen Helgedagsrud, Mikkel Næss, Henrik Kjus Alstad, Carsten Griwodz, Pål Halvorsen e Dag Johansen. "Processing Panorama Video in Real-time". International Journal of Semantic Computing 08, n. 02 (giugno 2014): 209–27. http://dx.doi.org/10.1142/s1793351x14400054.

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There are many scenarios where high resolution, wide field of view video is useful. Such panorama video may be generated using camera arrays where the feeds from multiple cameras pointing at different parts of the captured area are stitched together. However, processing the different steps of a panorama video pipeline in real-time is challenging due to the high data rates and the stringent timeliness requirements. In our research, we use panorama video in a sport analysis system called Bagadus. This system is deployed at Alfheim stadium in Tromsø, and due to live usage, the video events must be generated in real-time. In this paper, we describe our real-time panorama system built using a low-cost CCD HD video camera array. We describe how we have implemented different components and evaluated alternatives. The performance results from experiments ran on commodity hardware with and without co-processors like graphics processing units (GPUs) show that the entire pipeline is able to run in real-time.

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Zheng, Kun, Kang Zheng, Falin Fang, Hong Yao, Yunlei Yi e Deze Zeng. "Real-Time Massive Vector Field Data Processing in Edge Computing". Sensors 19, n. 11 (7 giugno 2019): 2602. http://dx.doi.org/10.3390/s19112602.

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The spread of the sensors and industrial systems has fostered widespread real-time data processing applications. Massive vector field data (MVFD) are generated by vast distributed sensors and are characterized by high distribution, high velocity, and high volume. As a result, computing such kind of data on centralized cloud faces unprecedented challenges, especially on the processing delay due to the distance between the data source and the cloud. Taking advantages of data source proximity and vast distribution, edge computing is ideal for timely computing on MVFD. Therefore, we are motivated to propose an edge computing based MVFD processing framework. In particular, we notice that the high volume feature of MVFD results in high data transmission delay. To solve this problem, we invent Data Fluidization Schedule (DFS) in our framework to reduce the data block volume and the latency on Input/Output (I/O). We evaluated the efficiency of our framework in a practical application on massive wind field data processing for cyclone recognition. The high efficiency our framework was verified by the fact that it significantly outperformed classical big data processing frameworks Spark and MapReduce.

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Catley, Christina, Kathy Smith, Carolyn McGregor, Andrew James e J. Mikael Eklund. "A Framework for Multidimensional Real-Time Data Analysis". International Journal of Computational Models and Algorithms in Medicine 2, n. 1 (gennaio 2011): 16–37. http://dx.doi.org/10.4018/jcmam.2011010102.

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In this paper, the authors present a framework to support multidimensional analysis of real-time physiological data streams and clinical data. The clinical context for the case study demonstration is neonatal intensive care, focusing specifically on the detection of episodes of central apnoea, a clinically significant problem. The model accounts for the multidimensional and real-time nature of apnoea of prematurity and the associated clinical rules. The framework demonstration includes: 1) defining rules that quantify concurrent behaviours between multiple synchronous data streams and asynchronous data values; 2) designing UML models to define present practice event processing for episodes of apnoea; 3) translating the model in SPADE to enable the deployment within the real-time processing layer of the Artemis platform, which utilizes IBM’s InfoSphere Streams; 4) demonstrating knowledge discovery with simple and complex temporal abstractions of the data streams; and 5) presenting results for early detection of episodes of apnoea across multiple physiological data streams.

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QI, Kai-Yuan, Zhuo-Feng ZHAO, Jun FANG e Qiang MA. "Real-Time Processing for High Speed Data Stream over Large Scale Data". Chinese Journal of Computers 35, n. 3 (15 agosto 2012): 477–90. http://dx.doi.org/10.3724/sp.j.1016.2012.00477.

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Zhengyi Yang, e Shilong Wang. "Model and Performance Analysis of Real-time Monitoring Data Processing". International Journal of Digital Content Technology and its Applications 6, n. 17 (30 settembre 2012): 595–602. http://dx.doi.org/10.4156/jdcta.vol6.issue17.65.

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Shahrivari, Saeed. "Beyond Batch Processing: Towards Real-Time and Streaming Big Data". Computers 3, n. 4 (17 ottobre 2014): 117–29. http://dx.doi.org/10.3390/computers3040117.

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