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Articles de revues sur le sujet « Data processing »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 25 juillet 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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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 (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 (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

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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 (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 (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 (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 (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 comprehen

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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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Gnip, P., and S. Kafka. "Using technology of data collection and data processing in precision farming." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 419–26. http://dx.doi.org/10.17221/5426-agricecon.

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Data collection, data processing, data presentation and data application in the System of Precision farming guarantee a success of this system in the market. Difficulties of technologies, which are currently and continually involved in this system, argue against its practical using by farmers. In this case, service company wants to create a suitable environment not only for data collection, but also for the high quality of the information distribution to customers. One of such tools is the MapServer placed on Internet web sites.

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Sharopova, Muxayyo Muxtor qizi. "PROCESSING TECHNOLOGIES BIG DATA." Multidisciplinary Journal of Science and Technology 4, no. 3 (2024): 390–95. https://doi.org/10.5281/zenodo.10870056.

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13

Stefanowicz, Bogdan, and Marek Cierpiał-Wolan. "Data processing errors." Wiadomości Statystyczne. The Polish Statistician 60, no. 9 (2015): 23–29. http://dx.doi.org/10.5604/01.3001.0014.8296.

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The article highlights the need to broaden the analysis of the quality of the survey results, taking into account the negative impact of certain operations of so-called editing input data, such as checking their accuracy and correction of errors. In the conclusions it underlines the need to extend the programs for academic lectures in statistics for analysis of the impact of processing operations on the quality of the results.

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14

Jaworski, John, and Elizabeth Bliss. "Data Processing Mathematics." Mathematical Gazette 71, no. 458 (1987): 334. http://dx.doi.org/10.2307/3617092.

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OHE, Shuzo. "Statistical Data Processing." Journal of the Japan Society of Colour Material 67, no. 9 (1994): 590–95. http://dx.doi.org/10.4011/shikizai1937.67.590.

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Sarychev, Dmitriy S. "Lidar data processing." SAPR i GIS avtomobilnykh dorog, no. 1(2) (2014): 16–19. http://dx.doi.org/10.17273/cadgis.2014.1.4.

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17

Pu, Wenjing. "Standardized Data Processing." Transportation Research Record: Journal of the Transportation Research Board 2338, no. 1 (2013): 44–57. http://dx.doi.org/10.3141/2338-06.

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18

Ahlswede, R., and P. Lober. "Quantum data processing." IEEE Transactions on Information Theory 47, no. 1 (2001): 474–78. http://dx.doi.org/10.1109/18.904565.

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19

Ahituv, Niv, Yeheskel Lapid, and Seev Neumann. "Processing encrypted data." Communications of the ACM 30, no. 9 (1987): 777–80. http://dx.doi.org/10.1145/30401.30404.

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Hagaman, Edward W., Jeffrey C. Hoch, and Alan S. Stern. "NMR Data Processing." Radiation Research 147, no. 2 (1997): 272. http://dx.doi.org/10.2307/3579432.

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21

Scherr, A. L. "Distributed data processing." IBM Systems Journal 38, no. 2.3 (1999): 354–74. http://dx.doi.org/10.1147/sj.382.0354.

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22

Satoh, Ichiro. "Pervasive Data Processing." Procedia Computer Science 63 (2015): 16–23. http://dx.doi.org/10.1016/j.procs.2015.08.307.

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23

Bouchachia, Abdelhamid. "Online data processing." Neurocomputing 126 (February 2014): 116–17. http://dx.doi.org/10.1016/j.neucom.2013.05.008.

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Cameron, David G. "Advanced data processing." Mikrochimica Acta 93, no. 1-6 (1987): 229–39. http://dx.doi.org/10.1007/bf01201692.

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Gough, T. G. "Data Processing Methods." Data Processing 27, no. 5 (1985): 51. http://dx.doi.org/10.1016/0011-684x(85)90145-5.

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Richards, B. "Data processing mathematics." Data Processing 28, no. 3 (1986): 162. http://dx.doi.org/10.1016/0011-684x(86)90015-8.

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Lepper, AM. "Data Processing Budgets." Data Processing 28, no. 2 (1986): 103. http://dx.doi.org/10.1016/0011-684x(86)90114-0.

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28

Campbell-Kelly, Martin. "Victorian data processing." Communications of the ACM 53, no. 10 (2010): 19–21. http://dx.doi.org/10.1145/1831407.1831417.

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29

Starck, J. L., A. Abergel, H. Aussel, et al. "ISOCAM data processing." Astronomy and Astrophysics Supplement Series 134, no. 1 (1999): 135–48. http://dx.doi.org/10.1051/aas:1999129.

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McIntyre, D. J. O. "NMR data processing." NMR in Biomedicine 12, no. 6 (1999): 405–6. http://dx.doi.org/10.1002/(sici)1099-1492(199910)12:6<405::aid-nbm590>3.0.co;2-c.

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Görlitz, L., B. H. Menze, B. M. Kelm, and F. A. Hamprecht. "Processing spectral data." Surface and Interface Analysis 41, no. 8 (2009): 636–44. http://dx.doi.org/10.1002/sia.3066.

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32

KRYVENCHUK, Yurii, and Mykhailo-Yurii KHANAS. "ALGORITHM OF DATA MINING AND PROCESSING OF RELATED DATA IN SOCIAL NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (2022): 115–18. http://dx.doi.org/10.31891/2307-5732-2022-311-4-115-118.

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We live in a time of rapid growth of information technology, which is firmly entrenched in our daily lives. It is simply impossible to imagine a modern person without social networks, because they perform a communicative and informational function, namely: communication, information retrieval, news exchange, etc. Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. So, there is a lot of information available to the user, which is difficult to identify something specific and necessary in the usual w

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33

Nikolova, Evgeniya, Mariya Monova-Zheleva, and Yanislav Zhelev. "Personal Data Processing in a Digital Educational Environment." Mathematics and Informatics LXV, no. 4 (2022): 365–78. http://dx.doi.org/10.53656/math2022-4-4-per.

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New technologies provide innovative spaces for cooperation and communication between employers and employees, citizens and structures, educators, and learners. Data protection issues have always been key to education providers, but the proliferation of online learning forms and formats poses new and unique challenges in this regard. When introducing a new technology that involves the collection of sensitive data, the General Data Protection Regulation (GDPR) of the European Parliament and the Council of the European Union requires the identification and mitigation of all risks that could lead

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34

Ganachari, Girish. "Event-Driven Data Processing for Business Intelligence Reporting." International Journal of Science and Research (IJSR) 8, no. 12 (2019): 2077–80. http://dx.doi.org/10.21275/sr24801085403.

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35

MARTYNIUK, Tatiana, Andrii KOZHEMIAKO, Bohdan KRUKIVSKYI, and Antonina BUDA. "ASSOCIATIVE OPERATIONS BASED ON DIFFERENCE-SLICE DATA PROCESSING." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (2022): 159–63. http://dx.doi.org/10.31891/2307-5732-2022-311-4-159-163.

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Associative operations are effectively used to solve such application problems as sorting, searching for certain features, and identifying extreme (maximum/minimum) elements in data sets. Thus, determining the maximum number as a result of sorting a numerical array is an acceptable operation in implementing the competition mechanism in neural networks. In addition, determining the average number in a numerical series by sorting significantly speeds up the process of median filtering of images and signals. In this case, the implementation of median filtering requires the use of sorting with the

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36

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

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

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Kalyan Uppala, Venkat. "Architecting a Cloud Data Platform: Bridging Storage and Compute for Enhanced Data Processing." International Journal of Science and Research (IJSR) 8, no. 1 (2019): 2281–89. http://dx.doi.org/10.21275/sr24810090304.

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38

Rostek, Katarzyna. "Data Analytical Processing in Data Warehouses." Foundations of Management 2, no. 1 (2010): 99–116. http://dx.doi.org/10.2478/v10238-012-0023-x.

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Data Analytical Processing in Data Warehouses The article presents issues connected with processing information from data warehouses (the analytical enterprise databases) and two basic types of analytical data processing in data warehouse. The genesis, main definitions, scope of application and real examples from business implementations will be described for each type of analysis. There will be presented copyrighted method of knowledge discovering in databases, together with practical guidelines for its proper and effective use in the enterprise.

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K., Dharani* &. Dr. G. Abel Thangaraja**. "BIG DATA PREPROCESSING USING ENHANCED DATA QUALITY RULES DISCOVERY MODEL (EDQRM)." International Journal of Engineering Research and Modern Education (IJERME) 8, no. 2 (2023): 33–41. https://doi.org/10.5281/zenodo.8428545.

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In the Big Data Era, data is the center for any governmental, institutional, and private organization. Endeavors were equipped towards extricating profoundly important bits of knowledge that can't occur assuming data is of low quality. Hence, data quality (DQ) is considered as a vital component in big data processing. In this stage, bad quality data isn't entered to the Big Data value chain. This paper, proposed the Enhanced data quality Rules discovery model (EDQRM) for assessment of quality and Big Data pre-processing. EDQRM discovery model to improve and precisely focus on the pre-p

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Rawish Siddiqui, Muhammad. "Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond." Chemistry Research and Practice 1, no. 2 (2024): 01–06. https://doi.org/10.64030/3065-906x.01.02.04.

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In the age of digital transformation, the rise of Big Data has fundamentally altered how organizations store, process, and utilize information. This whitepaper provides a comprehensive analysis comparing Big Data with traditional data systems, data warehousing, business intelligence (BI), artificial intelligence (AI), data science, and NoSQL databases. By exploring key differentiators such as volume, variety, velocity, and processing capabilities, this paper aims to shed light on how Big Data has reshaped modern technology infrastructures and its role in advancing analytics, decision-making, a

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Hwa Choi, Hyun, Kangho Kim, and Seung Jo Bae. "A Remote Memory System for High Performance Data Processing." International Journal of Future Computer and Communication 4, no. 1 (2015): 50–54. http://dx.doi.org/10.7763/ijfcc.2015.v4.354.

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Osborn, Wendy. "Unbounded Spatial Data Stream Query Processing using Spatial Semijoins." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 02 (2021): 33–41. http://dx.doi.org/10.5383/juspn.15.02.005.

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In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two s

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Proshin, A. A., A. M. Matveev, A. V. Kashnitskiy, and M. A. Burtsev. "Satellite data efficient processing with dynamic block archive access." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 17, no. 6 (2020): 56–60. http://dx.doi.org/10.21046/2070-7401-2020-17-6-56-60.

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44

Penížek, V., and L. Borůvka. "Processing of conventional soil survey data using geostatistical methods." Plant, Soil and Environment 50, No. 8 (2011): 352–57. http://dx.doi.org/10.17221/4043-pse.

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The aim of this study is to find a suitable treatment of conventional soil survey data for geostatistical exploitation. Different aims and methods of a conventional soil survey and the geostatistics can cause some problems. The spatial variability of clay content and pH for an area of 543 km<sup>2</sup> was described by variograms. First the original untreated data were used. Then the original data were treated to overcome the problems that arise from different aims of conventional soil survey and geostatistical approaches. Variograms calculated from the original data, both for cla

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45

MILOSAN, Ioan. "STATISTICAL PROCESSING OF EXPERIMENTAL DATA USING ANALYSIS OF VARIANCE." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 1 (2016): 489–96. http://dx.doi.org/10.19062/2247-3173.2016.18.1.67.

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46

More, Prof Vijay, Ms Ankita Shetty, and Ms Aishwarya Mapara Mr Rahul Ghuge Mr Rohit Sharma. "Employee Data Mining Based on Text and Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 379–81. http://dx.doi.org/10.31142/ijtsrd10791.

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Ch, Bilal Hussain. "Securing Cloud Data with the Application of Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 297–301. http://dx.doi.org/10.31142/ijtsrd18454.

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48

Maruddani, Baso, and Efri Sandi. "The Development of Ground Penetrating Radar (GPR) Data Processing." International Journal of Machine Learning and Computing 9, no. 6 (2019): 768–73. http://dx.doi.org/10.18178/ijmlc.2019.9.6.871.

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49

Ummatovich, Eshonqulov Sherzod. "DATA FILTERING IN THE IMAGE PROCESSING TOOLBOX(IPT) ENVIRONMENT." American Journal of Applied Science and Technology 4, no. 3 (2024): 24–28. http://dx.doi.org/10.37547/ajast/volume04issue03-05.

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Analysis of the Aydar-Arnasoy lake system in the environment of IPT. A brief hydrogeological description of the Aydar-Arnasoy lake system. Digital filtering of the Aydar-Arnasoy lake system image. Development of a digital model of the image of the Aydar-Arnasoy lake system using discrete Fourier transformation.

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50

Skoropad, Pylyp, and Andrii Yuras. "MACHINE LEARNING METHODS IN THERMOMETERS’ DATA EXTRACTION AND PROCESSING." Measuring Equipment and Metrology 85, no. 2 (2024): 40–45. http://dx.doi.org/10.23939/istcmtm2024.02.040.

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Research focuses on developing an all-encompassing algorithm for efficiently extracting, processing, and analyz- ing data about thermometers. The examination involves the application of a branch of artificial intelligence, in particular machine learning (ML) methods, as a means of automating processes. Such methods facilitate the identification and aggregation of pertinent data, the detection of gaps, and the conversion of unstructured text into an easily analyzable structured format. The paper details the employment of reinforcement learning for the automatic extraction of information from di

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