Academic literature on the topic 'Data processing'

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Journal articles on the topic "Data processing"

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Martha, Ranjith. "Real-Time Data Ingestion for Big Data Processing." International Journal of Science and Research (IJSR) 14, no. 2 (February 27, 2025): 570–72. https://doi.org/10.21275/sr25209075243.

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Mehraj, Nadiya, and Harveen Kour. "Data Processing Through Image Processing using Gaussian Minimum Shift Keying." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 977–81. http://dx.doi.org/10.31142/ijtsrd18819.

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Rossmann, Michael G., and Cornelis G. van Beek. "Data processing." Acta Crystallographica Section D Biological Crystallography 55, no. 10 (October 1, 1999): 1631–40. http://dx.doi.org/10.1107/s0907444999008379.

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X-ray diffraction data processing proceeds through indexing, pre-refinement of camera parameters and crystal orientation, intensity integration, post-refinement and scaling. TheDENZOprogram has set new standards for autoindexing, but no publication has appeared which describes the algorithm. In the development of the newData Processing Suite(DPS), one of the first aims has been the development of an autoindexing procedure at least as powerful as that used byDENZO. The resultant algorithm will be described. Another major problem which has arisen in recent years is scaling and post-refinement of data from different images when there are few, if any, full reflections. This occurs when the mosaic spread approaches or exceeds the angle of oscillation, as is usually the case for frozen crystals. A procedure which is able to obtain satisfactory results for such a situation will be described.
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Zasuhina, Ol'ga, Egor Ershov, Leonid Golovatiukov, and Grigory Shitenkov. "BIG DATA PROCESSING TECHNOLOGY." Bulletin of the Angarsk State Technical University 1, no. 16 (December 27, 2022): 98–100. http://dx.doi.org/10.36629/2686-777x-2022-1-16-98-100.

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Volkova, T., E. Furta, O. Dmitrieva, and I. Shabalina. "Pattern Building Methods in Genetic Data Processing." Journal on Selected Topics in Nano Electronics and Computing 1, no. 2 (June 2014): 2–6. http://dx.doi.org/10.15393/j8.art.2014.3041.

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Dayalan, Muthu. "MapReduce: Simplified Data Processing on Large Cluster." International Journal of Research and Engineering 5, no. 5 (April 2018): 399–403. http://dx.doi.org/10.21276/ijre.2018.5.5.4.

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Starukhin, Yaroslav, and Vladimir Diukarev. "AUTOMATION OF TEXT DATA PROCESSING USING NLP." American Journal of Engineering and Technology 6, no. 7 (July 1, 2024): 24–39. http://dx.doi.org/10.37547/tajet/volume06issue07-04.

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This study aims to develop an automated system for processing scientific texts using advanced NLP techniques. The methodology integrates classical NLP methods with deep learning approaches, employing SciBERT for text classification, LDA for topic modeling, and a modified TextRank algorithm for keyword extraction. Results demonstrate high accuracy in document classification (F1-score of 0.92), effective topic identification, and precise keyword extraction. The developed web interface showcases the system's practical applicability. This research contributes to the field by presenting a comprehensive solution for scientific text analysis, combining state-of-the-art language models with established NLP techniques. The study's novelty lies in its tailored approach to scientific literature, addressing the unique challenges of domain-specific language and complex content structure in academic texts.
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Seenivasan, Dhamotharan. "Real-Time Data Processing with Streaming ETL." International Journal of Science and Research (IJSR) 12, no. 11 (November 27, 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 (March 27, 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 (September 12, 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 co-relational to Hadoop is false; focus needs to be given on business value as well. Data Warehousing, Hadoop and stream processing complement each other very well. In this paper, we have tried reviewing a few frameworks and products which use real time data streaming by providing modifications to Hadoop.
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Dissertations / Theses on the topic "Data processing"

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Long, Christopher C. "Data Processing for NASA's TDRSS DAMA Channel." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611474.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California<br>Presently, NASA's Space Network (SN) does not have the ability to receive random messages from satellites using the system. Scheduling of the service must be done by the owner of the spacecraft through Goddard Space Flight Center (GSFC). The goal of NASA is to improve the current system so that random messages, that are generated on board the satellite, can be received by the SN. The messages will be requests for service that the satellites control system deems necessary. These messages will then be sent to the owner of the spacecraft where appropriate action and scheduling can take place. This new service is known as the Demand Assignment Multiple Access system (DAMA).
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Sun, Wenjun. "Parallel data processing for semistructured data." Thesis, London South Bank University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434394.

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Giordano, Manfredi. "Autonomic Big Data Processing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14837/.

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Apache Spark è un framework open source per la computazione distribuita su larga scala, caratterizzato da un engine in-memory che permette prestazioni superiori a soluzioni concorrenti nell’elaborazione di dati a riposo (batch) o in movimento (streaming). In questo lavoro presenteremo alcune tecniche progettate e implementate per migliorare l’elasticità e l’adattabilità del framework rispetto a modifiche dinamiche nell’ambiente di esecuzione o nel workload. Lo scopo primario di tali tecniche è di permettere ad applicazioni concorrenti di condividere le risorse fisiche disponibili nell’infrastruttura cluster sottostante in modo efficiente. Il contesto nel quale le applicazioni distribuite vengono eseguite difficilmente può essere considerato statico: le componenti hardware possono fallire, i processi possono interrompersi, gli utenti possono allocare risorse aggiuntive in modo imprevedibile nel tentativo di accelerare la computazione o di allegerire il carico di lavoro. Infine, non soltanto le risorse fisiche ma anche i dati in input possono variare di dimensione e complessità durante l’esecuzione, così che sia dati sia risorse non possano essere considerati statici. Una configurazione immutabile del cluster non riuscirà a ottenere la migliore efficienza possibile per tutti i differenti carichi di lavoro. Ne consegue che un framework per il calcolo distribuito che sia "consapevole" delle modifiche ambientali e delle modifiche al workload e che sia in grado di adattarsi a esse puo risultare piu performante di un framework che permetta unicamente configurazioni statiche. Gli esperimenti da noi compiuti con applicazioni Big Data altamente parallelizzabili mostrano come il costo della soluzione proposta sia minimo e come la nostra version di Spark più dinamica e adattiva possa portare a benefici in termini di flessibilità, scalabilità ed efficienza.
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Rydell, Joakim. "Advanced MRI Data Processing." Doctoral thesis, Linköping : Department of Biomedical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10038.

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Irick, Nancy. "Post Processing Data Analysis." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606091.

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ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada<br>Once the test is complete, the job of the Data Analyst has begun. Files from the various acquisition systems are collected. It is the job of the analyst to put together these files in a readable format so the success or failure of the test can be attained. This paper will discuss the process of breaking down these files, comparing data from different systems, and methods of presenting the data.
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Castro, Fernandez Raul. "Stateful data-parallel processing." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/31596.

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Democratisation of data means that more people than ever are involved in the data analysis process. This is beneficial - it brings domain-specific knowledge from broad fields - but data scientists do not have adequate tools to write algorithms and execute them at scale. Processing models of current data-parallel processing systems, designed for scalability and fault tolerance, are stateless. Stateless processing facilitates capturing parallelisation opportunities and hides fault tolerance. However, data scientists want to write stateful programs - with explicit state that they can update, such as matrices in machine learning algorithms - and are used to imperative-style languages. These programs struggle to execute with high-performance in stateless data-parallel systems. Representing state explicitly makes data-parallel processing at scale challenging. To achieve scalability, state must be distributed and coordinated across machines. In the event of failures, state must be recovered to provide correct results. We introduce stateful data-parallel processing that addresses the previous challenges by: (i) representing state as a first-class citizen so that a system can manipulate it; (ii) introducing two distributed mutable state abstractions for scalability; and (iii) an integrated approach to scale out and fault tolerance that recovers large state - spanning the memory of multiple machines. To support imperative-style programs a static analysis tool analyses Java programs that manipulate state and translates them to a representation that can execute on SEEP, an implementation of a stateful data-parallel processing model. SEEP is evaluated with stateful Big Data applications and shows comparable or better performance than state-of-the-art stateless systems.
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Nyström, Simon, and Joakim Lönnegren. "Processing data sources with big data frameworks." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188204.

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Big data is a concept that is expanding rapidly. As more and more data is generatedand garnered, there is an increasing need for efficient solutions that can be utilized to process all this data in attempts to gain value from it. The purpose of this thesis is to find an efficient way to quickly process a large number of relatively small files. More specifically, the purpose is to test two frameworks that can be used for processing big data. The frameworks that are tested against each other are Apache NiFi and Apache Storm. A method is devised in order to, firstly, construct a data flow and secondly, construct a method for testing the performance and scalability of the frameworks running this data flow. The results reveal that Apache Storm is faster than Apache NiFi, at the sort of task that was tested. As the number of nodes included in the tests went up, the performance did not always do the same. This indicates that adding more nodes to a big data processing pipeline, does not always result in a better performing setup and that, sometimes, other measures must be made to heighten the performance.<br>Big data är ett koncept som växer snabbt. När mer och mer data genereras och samlas in finns det ett ökande behov av effektiva lösningar som kan användas föratt behandla all denna data, i försök att utvinna värde från den. Syftet med detta examensarbete är att hitta ett effektivt sätt att snabbt behandla ett stort antal filer, av relativt liten storlek. Mer specifikt så är det för att testa två ramverk som kan användas vid big data-behandling. De två ramverken som testas mot varandra är Apache NiFi och Apache Storm. En metod beskrivs för att, för det första, konstruera ett dataflöde och, för det andra, konstruera en metod för att testa prestandan och skalbarheten av de ramverk som kör dataflödet. Resultaten avslöjar att Apache Storm är snabbare än NiFi, på den typen av test som gjordes. När antalet noder som var med i testerna ökades, så ökade inte alltid prestandan. Detta visar att en ökning av antalet noder, i en big data-behandlingskedja, inte alltid leder till bättre prestanda och att det ibland krävs andra åtgärder för att öka prestandan.
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Mai, Luo. "Towards efficient big data processing in data centres." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/64817.

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Large data processing systems require a high degree of coordination, and exhibit network bottlenecks due to massive communication data. This motivates my PhD study to propose system control mechanisms that improve monitoring and coordination, and efficient communication methods by bridging applications and networks. The first result is Chi, a new control plane for stateful streaming systems. Chi has a control loop that embeds control messages in data channels to seamlessly monitor and coordinate a streaming pipeline. This design helps monitor system and application-specific metrics in a scalable manner, and perform complex modification with on-the-fly data. The behaviours of control messages are customisable, thus enabling various control algorithms. Chi has been deployed into production systems, and exhibits high performance and scalability in test-bed experiments. With effective coordination, data-intensive systems need to remove network bottlenecks. This is important in data centres as their networks are usually over-subscribed. Hence, my study explores an idea that bridges applications and networks for accelerating communication. This idea can be realised (i) in the network core through a middlebox platform called NetAgg that can efficiently execute application-specific aggregation functions along busy network paths, and (ii) at network edges through a server network stack that provides powerful communication primitives and traffic management services. Test-bed experiments show that these methods can improve the communication of important analytics systems. A tight integration of applications and networks, however, requires an intuitive network programming model. My study thus proposes a network programming framework named Flick. Flick has a high-level programming language for application-specific network services. The services are compiled to dataflows and executed by a high-performance runtime. To be production-friendly, this runtime can run in commodity network elements and guarantee fair resource sharing among services. Flick has been used for developing popular network services, and its performance is shown in real-world benchmarks.
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Mueller, Guenter. "DIGITAL DATA RECORDING: NEW WAYS IN DATA PROCESSING." International Foundation for Telemetering, 2000. http://hdl.handle.net/10150/606505.

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International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California<br>With the introduction of digital data recorders new ways of data processing have been developed. The three most important improvements are discussed in this paper: A) By processing PCM Data from a digital recorder by using the SCSI-Interface our ground station has developed software to detect the synchronization pattern of the PCM data and then perform software frame decommutation. Many advantages will be found with this method. B) New digital recorders already use the CCSDS Standard as the internal recording format. Once this technique is implemented in our ground station’s software and becomes part of our software engineering team’s general know-how, the switch to CCSDS telemetry in the future will require no quantum leap in effort. C) Digital recorders offer a very new application: Writing data to a digital tape in the recorder’s own format, allows the replay of data using the recorder’s interfaces; i.e. writing vibration data from the host system to tape, using the analog format of the digital recorder, allows the analysis of the data either in analog form, using the analog interface of the recorder, or in digital form.
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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<br>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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Books on the topic "Data processing"

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Bourque, Linda, and Virginia Clark. Processing Data. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1992. http://dx.doi.org/10.4135/9781412985499.

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Anderson, Ronald Gordon. Data processing. 7th ed. London: Macdonald and Evans, 1990.

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Bingham, John. Data Processing. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9.

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Lester, Graham C. Data processing. London: Pitman, 1988.

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Lester, Graham C. Data processing. 3rd ed. London: Pitman, 1988.

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Technicians, Association of Accounting. Data processing. London: Financial Training, 1993.

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Bingham, John E. Data processing. 2nd ed. Basingstoke: Macmillan, 1989.

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Bingham, John E. Data processing. 2nd ed. London: Macmillan, 1989.

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Godse, Jay. Ruby Data Processing. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3474-7.

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Chang, Chein-I. Hyperspectral Data Processing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118269787.

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Book chapters on the topic "Data processing"

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Hofmann-Wellenhof, Bernhard, Herbert Lichtenegger, and James Collins. "Data processing." In Global Positioning System, 179–227. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-5126-6_9.

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Raggatt, Peter. "Data Processing." In Principles and Practice of Immunoassay, 190–218. London: Palgrave Macmillan UK, 1991. http://dx.doi.org/10.1007/978-1-349-11234-0_7.

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Singh, Pramod. "Data Processing." In Learn PySpark, 17–48. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4961-1_2.

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Kitchin, Christopher Robert. "Data Processing." In Telescopes and Techniques, 163–67. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3370-4_10.

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Tinnefeld, Christian. "Data Processing." In Building a Columnar Database on RAMCloud, 63–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20711-7_5.

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Johnson, Chris F. A., and Jayant Varma. "Data Processing." In Pro Bash Programming, 161–81. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0121-3_13.

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Hofmann-Wellenhof, Bernhard, Herbert Lichtenegger, and James Collins. "Data processing." In Global Positioning System, 201–80. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-3297-5_9.

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Hofmann-Wellenhof, Bernhard, Herbert Lichtenegger, and James Collins. "Data processing." In Global Positioning System, 199–253. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-3311-8_9.

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Akrill, Tim, and Stephen Osmond. "Data Processing." In Physics A Level, 195–206. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-13852-4_8.

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Kitchin, Chris. "Data Processing." In Telescopes and Techniques, 193–206. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0023-2_10.

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Conference papers on the topic "Data processing"

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He, Sheng, and Gang Zhang. "Geomagnetic Data Processing." In 2024 Photonics & Electromagnetics Research Symposium (PIERS), 1–3. IEEE, 2024. http://dx.doi.org/10.1109/piers62282.2024.10618623.

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Thakur, Samneet, Shubham Gupta, Pragya Arora, Parikshit Parasher, Raghav Mehra, Krishna Murari Agrawal, and Manavala Ramanujam Venugopalan. "NISAR SweepSAR Data Processing." In 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 1–4. IEEE, 2024. https://doi.org/10.1109/ingarss61818.2024.10984213.

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Hou, Shiyue, Nathan R. Tallent, Li Wang, and Ningfang Mi. "Performance Analysis of Data Processing in Distributed File Systems with Near Data Processing." In 2024 International Symposium on Networks, Computers and Communications (ISNCC), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/isncc62547.2024.10758994.

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Hassan, Syed Minhaj, Sudhakar Yalamanchili, and Saibal Mukhopadhyay. "Near Data Processing." In MEMSYS '15: International Symposium on Memory Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2818950.2818952.

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Ribeiro, Marcela X., Mônica R. P. Ferreira, Caetano Traina, and Agma J. M. Traina. "Data pre-processing." In the 5th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1456223.1456277.

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Satoh, Ichiro. "Edge Data Processing." In 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2016. http://dx.doi.org/10.1109/waina.2016.96.

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Voronkov, Maxim A., Ben Humphreys, Daniel Mitchell, and Matthew Whiting. "ASKAP data processing." In 2015 1st URSI Atlantic Radio Science Conference (URSI AT-RASC). IEEE, 2015. http://dx.doi.org/10.1109/ursi-at-rasc.2015.7303169.

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Spence, J. "Data processing challenges." In IET Seminar on Smart Metering - Gizmo or Revolutionary Technology? IEE, 2008. http://dx.doi.org/10.1049/ic:20080121.

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Alekseeva, D. "BIG DATA PROCESSING." In CHALLENGING ISSUES IN SYSTEMS MODELING AND PROCESSES, 13–17. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2025. https://doi.org/10.58168/cismp2024_13-17.

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Gyurjyan, V., A. Bartle, C. Lukashin, S. Mancilla, R. Oyarzun, and A. Vakhnin. "Component based dataflow processing framework." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363971.

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Reports on the topic "Data processing"

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Casasent, David. Optical Data Processing. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada174465.

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Conlin, Jeremy L., and Andrej Trkov. Nuclear Data Processing. IAEA Nuclear Data Section, November 2018. http://dx.doi.org/10.61092/iaea.c7t6-j2x8.

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SEA TECHNOLOGY ARLINGTON VA. Communications, Telemetry, Data Processing. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada417821.

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Andrews, Elisabeth. RACORO aerosol data processing. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1028128.

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- UC BERKELEY, M. SHEATS. ADVANCED DATA PROCESSING FOR VOLUMETRIC COMPUTED TOMOGRAPHY DATA. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/784592.

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Feng, Ya-Chien, Alyssa Matthews, Marqi Rocque, Mindy Deng, Timothy Wendler, Karen Johnson, Eddie Schuman, et al. TRACER Radar b1 Data Processing: Corrections, Calibrations, and Processing Report. Office of Scientific and Technical Information (OSTI), March 2024. http://dx.doi.org/10.2172/2326212.

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Matthews, Alyssa, Min Deng, Eddie Schuman, Ya-Chien Feng, and Marqui Rocque. SAIL Radar b1 Data Processing: Corrections, Calibrations, and Processing Report. Office of Scientific and Technical Information (OSTI), January 2025. https://doi.org/10.2172/2502058.

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Paterno, Marc, and Chris Green. Processing Contexts for Experimental HEP Data. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1422188.

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Macduff, M., and D. Egan. ACRF Data Collection and Processing Infrastructure. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/1020559.

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Fields, Erik, Karen Tracey, and D. R. Watts. Inverted Echo Sounder Data Processing Report. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada237576.

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