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Letteratura scientifica selezionata sul tema "Electronic data processing – Distributed processing"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 29 luglio 2024

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Articoli di riviste sul tema "Electronic data processing – Distributed processing"

1

A. M., Chernykh. "Blockchain and Processing of Judicial Data." Rossijskoe pravosudie, no. 9 (August 23, 2021): 54–62. http://dx.doi.org/10.37399/issn2072-909x.2021.9.54-62.

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. Improving the electronic document management system of the judicial system requires the use of new information technologies. Conducting trials with guaranteed protection of documentary data of all participants in the trial from changes or loss will reduce the corruption component, increase mutual confidence of the parties involved in the litigation in documents. An system analysis was made of the possibility of using a distributed registry of databases and building on its basis a secure document exchange network using blockchain technology. The work defines the directions of interaction of i

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Nazemi, Sepideh, Kin K. Leung, and Ananthram Swami. "Distributed Optimization Framework for In-Network Data Processing." IEEE/ACM Transactions on Networking 27, no. 6 (December 2019): 2432–43. http://dx.doi.org/10.1109/tnet.2019.2953581.

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Omar, Hoger Khayrolla, and Alaa Khalil Jumaa. "Distributed big data analysis using spark parallel data processing." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1505–15. http://dx.doi.org/10.11591/eei.v11i3.3187.

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Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used

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R.Kennady, Et al. "A Scalable and Economical Method for Distributed Data Processing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2 (February 25, 2023): 198–201. http://dx.doi.org/10.17762/ijritcc.v11i2.9832.

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This research paper presents a distributed data processing approach that involves the establishment of virtual machines, the creation of a distributed system, and the processing of data to obtain desired results. The proposed method aims to provide a simple and cost-effective solution for distributed data processing, with the ability to scale infrastructure according to the specific needs. Furthermore, a distributed data processing system is introduced, comprising virtual machines equipped with specialized software to facilitate the establishment of the distributed system. The method offers pr

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Benediktsson, Jon Atli, and Zebin Wu. "Distributed Computing for Remotely Sensed Data Processing [Scanning the Section]." Proceedings of the IEEE 109, no. 8 (August 2021): 1278–81. http://dx.doi.org/10.1109/jproc.2021.3094335.

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Atakishchev, O. I., M. V. Belov, I. S. Zakharov, and A. V. Nikolaev. "Specific Features of Parallel Asynchronous Data Processing in Distributed GIS." Telecommunications and Radio Engineering 64, no. 3 (2005): 167–75. http://dx.doi.org/10.1615/telecomradeng.v64.i3.10.

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Nokleby, Matthew, Haroon Raja, and Waheed U. Bajwa. "Scaling-Up Distributed Processing of Data Streams for Machine Learning." Proceedings of the IEEE 108, no. 11 (November 2020): 1984–2012. http://dx.doi.org/10.1109/jproc.2020.3021381.

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Li, Xin, Huayan Yu, Ligang Yuan, and Xiaolin Qin. "Query Optimization for Distributed Spatio-Temporal Sensing Data Processing." Sensors 22, no. 5 (February 23, 2022): 1748. http://dx.doi.org/10.3390/s22051748.

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The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper,

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Szmajduch, Magdalena. "Data and Task Scheduling in Distributed Computing Environments." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2014): 71–78. http://dx.doi.org/10.26636/jtit.2014.4.1049.

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Data-aware scheduling in today’s large-scale heterogeneous environments has become a major research and engineering issue. Data Grids (DGs), Data Clouds (DCs) and Data Centers are designed for supporting the processing and analysis of massive data, which can be generated by distributed users, devices and computing centers. Data scheduling must be considered jointly with the application scheduling process. It generates a wide family of global optimization problems with the new scheduling criteria including data transmission time, data access and processing times, reliability of the data servers

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Sestok, C. K., M. R. Said, and A. V. Oppenheim. "Randomized data selection in detection with applications to distributed signal processing." Proceedings of the IEEE 91, no. 8 (August 2003): 1184–98. http://dx.doi.org/10.1109/jproc.2003.814922.

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Tesi sul tema "Electronic data processing – Distributed processing"

1

Gottemukkala, Vibby. "Scalability issues in distributed and parallel databases." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/8176.

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Bodorik, Peter Carleton University Dissertation Engineering Electrical. "Query processing strategies in a distributed data base." Ottawa, 1985.

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3

Cline, George E. "A control framework for distributed (parallel) processing environments." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-12042009-020227/.

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Ravindran, K. "Reliable client-server communication in distributed programs." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/27514.

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Remote procedure call (RPC) and shared variable are communication abstractions which allow the various processes of a distributed program, often modelled as clients and servers, to communicate with one another across machine boundaries. A key requirement of the abstractions is to mask the machine and communication failures that may occur during the client-server communications. In practice, many distributed applications can inherently tolerate failures under certain situations. If such application layer information is available to the client-server communication layer (RPC and shared variable

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Robinson, Patrick Glen. "Distributed Linda : design, development, and characterization of the data subsystem /." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-07102009-040417/.

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Setiowijoso, Liono. "Data Allocation for Distributed Programs." PDXScholar, 1995. https://pdxscholar.library.pdx.edu/open_access_etds/5102.

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This thesis shows that both data and code must be efficiently distributed to achieve good performance in a distributed system. Most previous research has either tried to distribute code structures to improve parallelism or to distribute data to reduce communication costs. Code distribution (exploiting functional parallelism) is an effort to distribute or to duplicate function codes to optimize parallel performance. On the other hand, data distribution tries to place data structures as close as possible to the function codes that use it, so that communication cost can be reduced. In particular,

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Bernabéu-Aubán, José Manuel. "Location finding algorithms for distributed systems." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/32951.

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Navaratnam, Srivallipuranandan. "Reliable group communication in distributed systems." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26505.

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This work describes the design and implementation details of a reliable group communication mechanism. The mechanism guarantees that messages will be received by all the operational members of the group or by none of them (atomicity). In addition, the sequence of messages will be the same at each of the recipients (order). The message ordering property can be used to simplify distributed database systems and distributed processing algorithms. The proposed mechanism continues to operate despite process, host and communication link failures (survivability). Survivability is essential in fault-to

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Torres-Rojas, Francisco Jose. "Scalable approximations to causality and consistency of distributed objects." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/9155.

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Algire, Martin. "Distributed multi-processing for high performance computing." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31180.

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Parallel computing can take many forms. From a user's perspective, it is important to consider the advantages and disadvantages of each methodology. The following project attempts to provide some perspective on the methods of parallel computing and indicate where the tradeoffs lie along the continuum. Problems that are parallelizable enable researchers to maximize the computing resources available for a problem, and thus push the limits of the problems that can be solved. Solving any particular problem in parallel will require some very important design decisions to be made. These decisions ma

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Libri sul tema "Electronic data processing – Distributed processing"

1

J, Mullender Sape, ed. Distributed systems. 2nd ed. New York: ACM Press, 1993.

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2

IFIP/IEEE International Conference on Open Distributed Processing and Distributed Platforms (1997 Toronto, Ont.). Open distributed processing and distributed platforms: Proceedings of the IFIP/IEEE international conference on Open Distributed Processing and Distributed Platforms : 26-30 May 1997, Toronto, Canada. London: Chapman & Hall on behalf of the International Federation for Information Processing (IFIP), 1997.

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Varshney, Pramod K. Distributed detection and data fusion. Edited by Burrus C. S. Berlin: Springer, 1996.

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Varshney, Pramod K. Distributed detection and data fusion. Edited by Burrus C. S. New York: Springer, 1997.

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Crowcroft, Jon. Open distributed systems. London: UCL Press, 1995.

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Langsford, Alwyn. Distributed systems management. Wokingham, Eng: Addison-Wesley, 1993.

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Liu, M. L. Distributed computing: Principles and applications. Boston: Pearson/Addison Wesley, 2004.

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Bal, H. E. Programming distributed systems. Summit, NJ, USA: Silicon Press, 1990.

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9

T, Brazier F. M., Johansen D, and Institute of Electrical and Electronics Engineers., eds. Distributed open systems. Los Alamitos, Calif: IEEE Computer Society Press, 1994.

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Varshney, Pramod K. Distributed Detection and Data Fusion. New York, NY: Springer New York, 1997.

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Capitoli di libri sul tema "Electronic data processing – Distributed processing"

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Bingham, John. "Distributed Systems." In Data Processing, 245–55. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9_19.

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2

Buchanan, W. J. "Distributed Processing." In The Handbook of Data Communications and Networks, 83–110. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4020-7870-5_5.

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Walsh, Vincent. "Electronic Data Processing." In Computer Literacy, 75–78. London: Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-07674-1_13.

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Weik, Martin H. "distributed data processing." In Computer Science and Communications Dictionary, 443. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5393.

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Sakr, Sherif, Marcin Wylot, Raghava Mutharaju, Danh Le Phuoc, and Irini Fundulaki. "Distributed RDF Query Processing." In Linked Data, 51–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73515-3_4.

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Özsu, M. Tamer. "Distributed XML Processing." In Advances in Data and Web Management, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00672-2_1.

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Özsu, M. Tamer, and Patrick Valduriez. "Big Data Processing." In Principles of Distributed Database Systems, 449–518. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26253-2_10.

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Malley, Brian, Daniele Ramazzotti, and Joy Tzung-yu Wu. "Data Pre-processing." In Secondary Analysis of Electronic Health Records, 115–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43742-2_12.

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Weik, Martin H. "distributed data processing network." In Computer Science and Communications Dictionary, 443. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5394.

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Weik, Martin H. "distributed data processing system." In Computer Science and Communications Dictionary, 443. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5395.

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Atti di convegni sul tema "Electronic data processing – Distributed processing"

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Zhang, Yongli, Tailin Han, Xingye Liu, and Siqi Tian. "Shock wave signal distributed acquisition and data processing technology." In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054027.

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Plattner, Markus, Sabine Ott, Sebastian Albrecht, Jintin Tran, Christopher Mandla, Jan-Christoph Tenzer, Thomas Schanz, et al. "ESA Athena WFI Onboard Electronics - Distributed Control and Data Processing." In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2020. http://dx.doi.org/10.23919/date48585.2020.9116484.

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Munerman, Victor, and Daniel Munerman. "Realization of Distributed Data Processing on the Basis of Container Technology." In 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2019. http://dx.doi.org/10.1109/eiconrus.2019.8656766.

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Furuichi, Tetsuo, and Hiroshi Mineno. "Distributed data processing method for next generation IoT system." In 2016 IEEE 5th Global Conference on Consumer Electronics. IEEE, 2016. http://dx.doi.org/10.1109/gcce.2016.7800441.

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Dai, Qinglong, and Jin Qian. "A Distributed Stream Data Processing Platform Design and Implementation in Smart Cities." In 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2020. http://dx.doi.org/10.1109/iceict51264.2020.9334234.

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Kamenskikh, I. S., D. M. Sinelnikov, D. S. Kalintsev, A. A. Kozlov, M. M. Rovnyagin, and D. A. Shulga. "Software development framework for a distributed storage and GPGPU data processing infrastructure." In 2016 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW). IEEE, 2016. http://dx.doi.org/10.1109/eiconrusnw.2016.7448158.

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Jiang, Bing, and Kun Huang. "The Design of Cloud Computing Platform for Massive Data Processing of Distributed Photovoltaic Power." In 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/meic-15.2015.161.

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Zheng, Ting, and Chuan He. "Design of real-time distributed data collection and analysis system." In International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), edited by Valentina E. Balas and Zeashan Hameed Khan. SPIE, 2023. http://dx.doi.org/10.1117/12.2682274.

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Ananth, K. R., Hari Shankar Punna, Krishnamoorthy Selvaraj, Rajagopal K, Vaishali Mahajan, and Sakthivel S. "Green IoT Edge Computing Towards Sustainable and Distributed Data Processing." In 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2023. http://dx.doi.org/10.1109/upcon59197.2023.10434734.

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Chen, Yihui, and Xue Tang. "Computer Vision and Image Processing Under Big Data Analysis." In 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2024. http://dx.doi.org/10.1109/icdcece60827.2024.10548841.

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Rapporti di organizzazioni sul tema "Electronic data processing – Distributed processing"

1

Overberg, Mark E., Kent Martin Geib, Darwin Keith Serkland, Alan Yuan-Chun Hsu, Gordon Arthur Keeler, and Patrick Sean Finnegan. Electronic/photonic interfaces for ultrafast data processing. Office of Scientific and Technical Information (OSTI), September 2008. http://dx.doi.org/10.2172/940521.

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Igor V. Terekhov. Distributed processing and analysis of physics data in the D0 SAM system at Fermilab. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/785150.

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Ramchandran, Kannan, and Kristofer Pister. Sensor Webs of SmartDust: Distributed Signal Processing/Data Fusion/Inferencing in Large Microsensor Arrays. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422190.

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Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.

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Modlo, Yevhenii O., Serhiy O. Semerikov, Pavlo P. Nechypurenko, Stanislav L. Bondarevskyi, Olena M. Bondarevska, and Stanislav T. Tolmachev. The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3264.

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Computer simulation of technical objects and processes is one of the components of the system of professional training of a modern electromechanics engineer. It has been established that despite the fact that mobile Internet devices (MID) are actively used by electrical engineers, the methods of using them in the process of bachelor in electromechanics training is considered only in some domestic scientific studies. The article highlights the components of the methods of using MID in the formation of the ICT component of the competence of the bachelor in electromechanics in modeling of technic

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Leathers, Emily, Clayton Thurmer, and Kendall Niles. Encryption for edge computing applications. Engineer Research and Development Center (U.S.), May 2024. http://dx.doi.org/10.21079/11681/48596.

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As smart sensors and the Internet of Things (IoT) exponentially expand, there is an increased need for effective processing solutions for sensor node data located in the operational arena where it can be leveraged for immediate decision support. Current developments reveal that edge computing, where processing and storage are performed close to data generation locations, can meet this need (Ahmed and Ahmed 2016). Edge computing imparts greater flexibility than that experienced in cloud computing architectures (Khan et al. 2019). Despite these benefits, the literature highlights open security i

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Borgwardt, Stefan, Walter Forkel, and Alisa Kovtunova. Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.223.

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Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language ELH⊥, which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a com

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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Avis, William. Drivers, Barriers and Opportunities of E-waste Management in Africa. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/k4d.2022.016.

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Population growth, increasing prosperity and changing consumer habits globally are increasing demand for consumer electronics. Further to this, rapid changes in technology, falling prices and consumer appetite for better products have exacerbated e-waste management challenges and seen millions of tons of electronic devices become obsolete. This rapid literature review collates evidence from academic, policy focussed and grey literature on e-waste management in Africa. This report provides an overview of constitutes e-waste, the environmental and health impacts of e-waste, of the barriers to ef

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Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Micrometeorological methods for inferring whole canopy evapotranspiration in large agricultural structures: measurements and modeling. United States Department of Agriculture, October 2015. http://dx.doi.org/10.32747/2015.7594402.bard.

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Original objectives and revisions The original objectives as stated in the approved proposal were: (1) To establish guidelines for the use of micrometeorological techniques as accurate, reliable and low-cost tools for continuous monitoring of whole canopy ET of common crops grown in large agricultural structures. (2) To adapt existing methods for protected cultivation environments. (3) To combine previously derived theoretical models of air flow and scalar fluxes in large agricultural structures (an outcome of our previous BARD project) with ET data derived from application of turbulent transp

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