Thematische Bibliographien / Real-time data processing

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Auswahl der wissenschaftlichen Literatur zum Thema „Real-time data processing“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "Real-time data processing"

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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Dissertationen zum Thema "Real-time data processing"

Ostroumov, Ivan Victorovich. "Real time sensors data processing." Thesis, Polit. Challenges of science today: XIV International Scientific and Practical Conference of Young Researchers and Students, April 2–3, 2014 : theses. – К., 2014. – 35p, 2014. http://er.nau.edu.ua/handle/NAU/26582.

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Sensor it is the most powerful part of any system. Aviation industry is the plase where milions of sensors is be used for difetrent purpuses. Othe wery important task of avionics equipment is data transfer between sensors to processing equipment. Why it is so important to transmit data online into MatLab? Nowadays rapidly are developing unmanned aerial vehicles. If we can transmit data from UAV sensors into MatLab, then we can process it and get the desired information about UAV. Of course we have to use the most chipiest way to data transfer. Today everyone in the world has mobile phone

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White, Allan P., and Richard K. Dean. "Real-Time Test Data Processing System." International Foundation for Telemetering, 1989. http://hdl.handle.net/10150/614650.

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International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California The U.S. Army Aviation Development Test Activity at Fort Rucker, Alabama needed a real-time test data collection and processing capability for helicopter flight testing. The system had to be capable of collecting and processing both FM and PCM data streams from analog tape and/or a telemetry receiver. The hardware and software was to be off the shelf whenever possible. The integration was to result in a stand alone telemetry collection and proces

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Macias, Filiberto. "Real Time Telemetry Data Processing and Data Display." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611405.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California The Telemetry Data Center (TDC) at White Sands Missile Range (WSMR) is now beginning to modernize its existing telemetry data processing system. Modern networking and interactive graphical displays are now being introduced. This infusion of modern technology will allow the TDC to provide our customers with enhanced data processing and display capability. The intent of this project is to outline this undertaking.

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Dowling, Jason, John Welling, Loral Aerosys, Kathy Nanzetta, Toby Bennett, and Jeff Shi. "ACCELERATING REAL-TIME SPACE DATA PACKET PROCESSING." International Foundation for Telemetering, 1995. http://hdl.handle.net/10150/608429.

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International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada NASA’s use of high bandwidth packetized Consultative Committee for Space Data Systems (CCSDS) telemetry in future missions presents a great challenge to ground data system developers. These missions, including the Earth Observing System (EOS), call for high data rate interfaces and small packet sizes. Because each packet requires a similar amount of protocol processing, high data rates and small packet sizes dramatically increase the real-time workload on ground packet pr

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Liu, Guangtian. "An event service architecture in distributed real-time systems /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Dreibelbis, Harold N., Dennis Kelsch, and Larry James. "REAL-TIME TELEMETRY DATA PROCESSING and LARGE SCALE PROCESSORS." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612912.

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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada Real-time data processing of telemetry data has evolved from a highly centralized single large scale computer system to multiple mini-computers or super mini-computers tied together in a loosely coupled distributed network. Each mini-computer or super mini-computer essentially performing a single function in the real-time processing sequence of events. The reasons in the past for this evolution are many and varied. This paper will review some of the more signific

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Feather, Bob, and Michael O’Brien. "OPEN ARCHITECTURE SYSTEM FOR REAL TIME TELEMETRY DATA PROCESSING." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612934.

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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada There have been many recent technological advances in small computers, graphics stations, and system networks. This has made it possible to build highly advanced distributed processing systems for telemetry data acquisition and processing. Presently there is a plethora of vendors marketing powerful new network workstation hardware and software products. Computer vendors are rapidly developing new products as new technology continues to emerge. It is becoming diff

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Dahan, Michael. "RTDAP: Real-Time Data Acquisition, Processing and Display System." International Foundation for Telemetering, 1989. http://hdl.handle.net/10150/614629.

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International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California This paper describes a data acquisition, processing and display system which is suitable for various telemetry applications. The system can be connected either to a PCM encoder or to a telemetry decommutator through a built-in interface and can directly address any channel from the PCM stream for processing. Its compact size and simplicity allow it to be used in the flight line as a test console, in mobile stations as the main data processing sys

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Spina, Robert. "Real time maze traversal /." Online version of thesis, 1989. http://hdl.handle.net/1850/10566.

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Ghosh, Kaushik. "Speculative execution in real-time systems." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/8174.

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Bücher zum Thema "Real-time data processing"

1951-, Halang Wolfgang A., Stoyenko Alexander D. 1962-, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Study Institute on Real Time Computing (1992 : Sint Maarten, Netherlands Antilles), eds. Real time computing. Springer-Verlag, 1994.

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Jan, Wikander, and Svensson Bertil 1954-, eds. Real-time systems in mechatronic applications. Kluwer Academic Publishers, 1998.

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Krishna, C. M. Real-time systems. McGraw-Hill, 1997.

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Lee, Y. H. Readings in real-time systems. IEEE Computer Society Press, 1993.

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1945-, Brown Christopher M., and Terzopoulos Demetri, eds. Real-time computer vision. Cambridge University Press, 1995.

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Brett, Tjaden, and Welch Lonnie R, eds. Real-time system security. Nova Science Pub., 2003.

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Armstrong, Philip N. Data rearrangement and real-time computation. Rand Corp., 1993.

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-P, Tsai Jeffrey J., ed. Distributed real-time systems: Monitoring, visualization, debugging, and analysis. Wiley, 1996.

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1958-, Haines Eric, ed. Real-time rendering. A K Peters, 1999.

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Motus, L. Timing analysis of real-time software. Pergamon, 1994.

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Buchteile zum Thema "Real-time data processing"

Fournier, Fabiana, and Inna Skarbovsky. "Real-Time Data Processing." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_11.

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AbstractTo remain competitive, organizations are increasingly taking advantage of the high volumes of data produced in real time for actionable insights and operational decision-making. In this chapter, we present basic concepts in real-time analytics, their importance in today’s organizations, and their applicability to the bioeconomy domains investigated in the DataBio project. We begin by introducing key terminology for event processing, and motivation for the growing use of event processing systems, followed by a market analysis synopsis. Thereafter, we provide a high-level overview of eve

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Weik, Martin H. "real-time data processing." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15596.

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Bingham, John. "On-Line and Real Time Systems." In Data Processing. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9_18.

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Wingerath, Wolfram, Norbert Ritter, and Felix Gessert. "General-Purpose Stream Processing." In Real-Time & Stream Data Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10555-6_5.

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Attoui, Ammar. "Principles of Real-Time Data Processing." In Practitioner Series. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0463-6_5.

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Paterson, M. "Real-Time Data Processing for SuperCOSMOS." In Astrophysics and Space Science Library. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2472-0_19.

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Wiederhold, Gio, and Paul D. Clayton. "Processing Biological Data in Real Time." In M. D. Computing: Benchmark Papers. Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4710-4_13.

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Yadav, Vinit. "Real-Time Analytics with Storm." In Processing Big Data with Azure HDInsight. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2869-2_7.

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Zhao, Bo, Cheng Cheng, Yuxin Cai, and Tang Zhiwei. "Real-Time Image Processing System." In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_232.

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Fox, Geoffrey C., Mehmet S. Aktas, Galip Aydin, et al. "Grids for Real Time Data Applications." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_39.

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Konferenzberichte zum Thema "Real-time data processing"

Garimella, Sarvesh, and Douglas Franz. "Satellite edge AI for automated training data collection, transfer learning, and synthetic training data generation." In Real-Time Image Processing and Deep Learning 2025, edited by Nasser Kehtarnavaz and Mukul V. Shirvaikar. SPIE, 2025. https://doi.org/10.1117/12.3053567.

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Sun, Xiaoyang, Feng Wang, Yong Wang, and Shi Li. "Data processing for EAST remote participation." In 2016 IEEE-NPSS Real Time Conference (RT). IEEE, 2016. http://dx.doi.org/10.1109/rtc.2016.7543126.

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Kaixin, Shen, Honglei An, Huang Yongshan, Wei Qing, and Ma HongXu. "Visual Real-time Data Processing." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164097.

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Vinitski, S., U. Szumowski, and R. H. Griffey. "Real time NMR data processing." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94544.

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Makowski, D., A. Mielczarek, P. Perek, et al. "High-speed data processing module for LLRF." In 2014 IEEE-NPSS Real Time Conference (RT). IEEE, 2014. http://dx.doi.org/10.1109/rtc.2014.7097409.

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Gu, Minhao, Kejun Zhu, Fei Li, and Wei Shen. "TaskRouter: A newly designed online data processing framework." In 2016 IEEE-NPSS Real Time Conference (RT). IEEE, 2016. http://dx.doi.org/10.1109/rtc.2016.7543088.

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Barrera, E., M. Ruiz, S. Lopez, D. Machon, and J. Vega. "PXI-based architecture for real time data acquisition and distributed dynamical data processing." In 14th IEEE-NPSS Real Time Conference, 2005. IEEE, 2005. http://dx.doi.org/10.1109/rtc.2005.1547509.

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Mousessian, Ardvas, and Christina Vuu. "Near real time data processing system." In Optical Engineering + Applications, edited by Philip E. Ardanuy and Jeffery J. Puschell. SPIE, 2008. http://dx.doi.org/10.1117/12.800641.

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Durbin, Phillip, Curt Tilmes, Brian Duggan, and Bigyani Das. "OMI Near Real Time data processing." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651380.

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Svingos, Christoforos, Theofilos Mailis, Herald Kllapi, Lefteris Stamatogiannakis, Yannis Kotidis, and Yannis Ioannidis. "Real time processing of streaming and static information." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840631.

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Berichte der Organisationen zum Thema "Real-time data processing"

Fiori, R. A. D., K. Reiter, D. Galeschuk, T. Ghosal, and N. Olfert. Near real-time processing of NRCan riometer data. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332078.

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Owechko, Yuri, and Bernard Soffer. Real-Time Implementation of Nonlinear Optical Data Processing Functions. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada233521.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada251567.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada259120.

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Roth, Christopher J., Nelson A. Bonito, Maurice F. Tautz, and Eugene C. Courtney. CHAWS Data Processing and Analysis Tools in Real-Time and Postflight Environments. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada381118.

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Birkemeier, William, Kent Hathaway, Michael Forte, Katherine Brodie, Patrick Dickhudt, and Annika O'Dea. Field Research Facility long-term data. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49713.

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The U.S. Army Corps of Engineers has been collecting long-term coastal process data at the Engineer Research and Development Center, Field Research Facility in Duck, NC since 1974. These data include meteorological, oceanographic, topographic, and bathymetric data. Oceanographic and meteorologic data are collected and processed as real-time data and follow Qartod (https://ioos.noaa.gov/project/qartod/) real-time data quality control standards. Dune lidar data including both DEMs and hydrodynamic data are collected and processed in real. These data are stored and publicly served in netCDF forma

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Desai, Jairaj, Rahul Suryakant Sakhare Sakhare, Justin Mahlberg, Jijo K. Mathew, Howell Li, and Darcy M. Bullock. Implementation of Enhanced Probe Data (CANBUS) for Tactical Workzone and Winter Operations Management. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317643.

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For over a decade, segment-based probe data has been extensively used by transportation stakeholders for monitoring mobility on Indiana roadways. However, enhanced probe data from connected vehicles includes a richer dataset that can provide more detailed real-time and after-action reviews. This enhanced data includes detailed vehicle trajectories, at 3s resolution, and “event data.” This event data is near real-time and includes hard-braking events, hard-acceleration events, weather-related data, including wiper activations and some seat belt usage data. This project developed a set of method

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Navarro, Luke, Shea Hammond, and Richard Johansen. Sensor fusion for aerial robotic system. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49701.

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As uncrewed aerial vehicle (drone) use expands across industries so also does the complexity of sensor payloads. At present, there are no commercially available products for the management and fusion of multisensor data. Sensor Fusion for Aerial Robotic Systems (SFARS) is a sensor agnostic, modular platform for intelligent multisensor data fusion and processing. At the time of writing, SFARS exists as a root codebase, a PC application for processing of previously collected drone data and as a prototype hardware platform for real-time drone deployment. This report serves as a technical users gu

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Kong, Zhihao, and Na Lu. Field Implementation of Concrete Strength Sensor to Determine Optimal Traffic Opening Time. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317724.

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In the fast-paced and time-sensitive fields of construction and concrete production, real-time monitoring of concrete strength is crucial. Traditional testing methods, such as hydraulic compression (ASTM C 39) and maturity methods (ASTM C 1074), are often laborious and challenging to implement on-site. Building on prior research (SPR 4210 and SPR 4513), we have advanced the electromechanical impedance (EMI) technique for in-situ concrete strength monitoring, crucial for determining safe traffic opening times. These projects have made significant strides in technology, including the development

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Selvaraju, Ragul, SHABARIRAJ SIDDESWARAN, and Hariharan Sankarasubramanian. The Validation of Auto Rickshaw Model for Frontal Crash Studies Using Video Capture Data. SAE International, 2020. http://dx.doi.org/10.4271/2020-28-0490.

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Despite being Auto rickshaws are the most important public transportation around Asian countries and especially in India, the safety standards and regulations have not been established as much as for the car segment. The Crash simulations have evolved to analyze the vehicle crashworthiness since crash experimentations are costly. The work intends to provide the validation for an Auto rickshaw model by comparing frontal crash simulation with a random head-on crash video. MATLAB video processing tool has been used to process the crash video, and the impact velocity of the frontal crash is obtain

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