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Articoli di riviste sul tema "Distributed processing"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 9 giugno 2025

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1

Fox, Peter T., and Karl J. Friston. "Distributed processing; distributed functions?" NeuroImage 61, no. 2 (June 2012): 407–26. http://dx.doi.org/10.1016/j.neuroimage.2011.12.051.

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2

ME, E. Sankaran. "Distributed Control Systems in Food Processing." International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (December 31, 2018): 27–30. http://dx.doi.org/10.31142/ijtsrd18921.

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3

Stewart, Ian. "Highly distributed processing." Nature 337, no. 6202 (January 1989): 13. http://dx.doi.org/10.1038/337013a0.

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4

Scherr, A. L. "SAA distributed processing." IBM Systems Journal 27, no. 3 (1988): 370–83. http://dx.doi.org/10.1147/sj.273.0370.

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5

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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6

Bowen, Dyfed. "Open distributed processing." Computer Networks and ISDN Systems 23, no. 1-3 (January 1991): 195–201. http://dx.doi.org/10.1016/0169-7552(91)90107-n.

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7

Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, Furqan Aziz, Bader Alouffi, M. Irfan Uddin, and Ahmed AlGhamdi. "Efficient Processing of Image Processing Applications on CPU/GPU." Mathematical Problems in Engineering 2020 (October 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/4839876.

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Abstract (sommario):
Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.
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8

Sutherland, Stuart. "Cognition: Parallel distributed processing." Nature 323, no. 6088 (October 1986): 486. http://dx.doi.org/10.1038/323486a0.

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9

Nierstrasz, Oscar, Alan Snyder, Anthony S. Williams, and William Cook. "Open distributed processing (panel)." ACM SIGPLAN OOPS Messenger 5, no. 2 (April 1994): 67–71. http://dx.doi.org/10.1145/260304.260322.

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10

KRITHIVASAN, KAMALA, N. SAKTHI BALAN, and PRAHLADH HARSHA. "DISTRIBUTED PROCESSING IN AUTOMATA." International Journal of Foundations of Computer Science 10, no. 04 (December 1999): 443–63. http://dx.doi.org/10.1142/s0129054199000319.

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With distributed computing beginning to play a major role in modern Computer Science, the theory of grammar systems and distributed automata has been developed in order to model distributed computing. In this paper, we introduce the notion of distributed automata in the sequential sense. Distributed Automata are a group of automata working in unison to accept one language. We build the theory of distributed for FSA and PDA in different modes of acceptance like the t-mode, *-mode, =k-mode, ≤k-mode and ≥k-mode. We then analyze the acceptance power of each automata in all the above modes. We present proofs that distributed FSAs do not have any additional power over "centralized" FSAs in any of the modes, while distributed PDAs with only two components are as powerful as Turing Machines in all of the modes. We give proofs for the equivalence of all modes in the case of PDAs. We also study a restricted version of distributed PDA called k-turn distributed PDA.
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11

Smith, J. E. "Instruction-level distributed processing." Computer 34, no. 4 (April 2001): 59–65. http://dx.doi.org/10.1109/2.917541.

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12

Kolcun, Roman, David E. Boyle, and Julie A. McCann. "Efficient Distributed Query Processing." IEEE Transactions on Automation Science and Engineering 13, no. 3 (July 2016): 1230–46. http://dx.doi.org/10.1109/tase.2016.2530941.

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13

Dawson, Michael R. W., and Don P. Schopflocher. "Autonomous processing in parallel distributed processing networks." Philosophical Psychology 5, no. 2 (January 1992): 199–219. http://dx.doi.org/10.1080/09515089208573056.

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14

Hoppen, Martin, Ralf Waspe, Malte Rast, and Juergen Rossmann. "Distributed Information Processing and Rendering for 3D Simulation Applications." International Journal of Computer Theory and Engineering 6, no. 3 (2014): 247–53. http://dx.doi.org/10.7763/ijcte.2014.v6.870.

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15

Bar-Ness, Yeheskel, Petar Popovski, Osvaldo Simeone, and Umberto Spagnolini. "Distributed processing for wireless networks." Journal of Communications and Networks 11, no. 4 (August 2009): 323–26. http://dx.doi.org/10.1109/jcn.2009.6391345.

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16

Schlichter, Johann. "Collaboration in distributed document processing." ACM SIGOIS Bulletin 13, no. 1 (April 1992): 32–33. http://dx.doi.org/10.1145/130643.130682.

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17

Wolfson, Ouri, and Avi Silberschatz. "Distributed processing of logic programs." ACM SIGMOD Record 17, no. 3 (June 1988): 329–36. http://dx.doi.org/10.1145/971701.50242.

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18

Frasciello, Michael J., and John Richardson. "Distributed processing and Windows NT." Library Consortium Management: An International Journal 1, no. 3/4 (December 1999): 76–83. http://dx.doi.org/10.1108/14662769910305768.

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19

Ye, Haiwei, Brigitte Kerhervé, and Gregor V. Bochmann. "QoS-based Distributed Query Processing." Ingénierie des systèmes d'information 9, no. 5-6 (December 24, 2004): 205–34. http://dx.doi.org/10.3166/isi.9.5-6.205-234.

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20

Tamura, Shinsuke, Yasukuni Okataku, and Toshibumi Seki. "Intellectual Distributed Processing System Architechture." IEEJ Transactions on Electronics, Information and Systems 108, no. 6 (1988): 393–400. http://dx.doi.org/10.1541/ieejeiss1987.108.6_393.

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21

Daniels, Dean S., Alfred Z. Spector, and Dean S. Thompson. "Distributed logging for transaction processing." ACM SIGMOD Record 16, no. 3 (December 1987): 82–96. http://dx.doi.org/10.1145/38714.38728.

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22

Kennedy, Scott D., and Andrew B. Schwartz. "Distributed processing of movement signaling." Proceedings of the National Academy of Sciences 116, no. 52 (December 23, 2019): 26266–73. http://dx.doi.org/10.1073/pnas.1902296116.

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Abstract (sommario):
Basic neurophysiological research with monkeys has shown how neurons in the motor cortex have firing rates tuned to movement direction. This original finding would have been difficult to uncover without the use of a behaving primate paradigm in which subjects grasped a handle and moved purposefully to targets in different directions. Subsequent research, again using behaving primate models, extended these findings to continuous drawing and to arm and hand movements encompassing action across multiple joints. This research also led to robust extraction algorithms in which information from neuronal populations is used to decode movement intent. The ability to decode intended movement provided the foundation for neural prosthetics in which brain-controlled interfaces are used by paralyzed human subjects to control computer cursors or high-performance motorized prosthetic arms and hands. This translation of neurophysiological laboratory findings to therapy is a clear example of why using nonhuman primates for basic research is valuable for advancing treatment of neurological disorders. Recent research emphasizes the distribution of intention signaling through neuronal populations and shows how many movement parameters are encoded simultaneously. In addition to direction and velocity, the arm’s impedance has now been found to be encoded as well. The ability to decode motion and force from neural populations will make it possible to extend neural prosthetic paradigms to precise interaction with objects, enabling paralyzed individuals to perform many tasks of daily living.
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23

Jayasekara, Sachini, Sameera Kannangara, Tishan Dahanayakage, Isuru Ranawaka, Srinath Perera, and Vishaka Nanayakkara. "Wihidum: Distributed complex event processing." Journal of Parallel and Distributed Computing 79-80 (May 2015): 42–51. http://dx.doi.org/10.1016/j.jpdc.2015.03.002.

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24

Zajac, Bernard P. "Distributed data processing: New problems." Computers & Security 7, no. 3 (June 1988): 249–50. http://dx.doi.org/10.1016/0167-4048(88)90027-2.

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25

SAYED, A. H., and C. G. LOPES. "Adaptive Processing over Distributed Networks." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E90-A, no. 8 (August 1, 2007): 1504–10. http://dx.doi.org/10.1093/ietfec/e90-a.8.1504.

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26

Tsuchiya, M., and Douglas C. Shannon. "Simulation of distributed processing networks." Computer Networks and ISDN Systems 11, no. 1 (January 1986): 15–27. http://dx.doi.org/10.1016/0169-7552(86)90026-7.

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27

Casazza, Peter G., Gitta Kutyniok, and Shidong Li. "Fusion frames and distributed processing." Applied and Computational Harmonic Analysis 25, no. 1 (July 2008): 114–32. http://dx.doi.org/10.1016/j.acha.2007.10.001.

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28

Hui, S. C., K. Y. Chan, and G. Y. Qian. "Distributed fax message processing system." Journal of Network and Computer Applications 20, no. 2 (April 1997): 171–90. http://dx.doi.org/10.1006/jnca.1997.0045.

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29

Segev, Arie. "Algorithms for distributed data processing." Computers & Operations Research 14, no. 2 (January 1987): 145–62. http://dx.doi.org/10.1016/0305-0548(87)90006-2.

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30

Venkatasubramanian, S. "Green IoT Edge Computing Towards Sustainable and Distributed Data Processing." International Journal of Research Publication and Reviews 4, no. 9 (September 2023): 413–36. http://dx.doi.org/10.55248/gengpi.4.923.52454.

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31

Yamasaki, Nobuyuki, and Toshihiro Matsui. "Functionally distributed responsive micro controller for distributed real-time processing." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 429. http://dx.doi.org/10.1016/s0920-5489(99)90857-7.

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32

Tran, Tri Minh, and Byung Suk Lee. "Distributed Adaptive Windowed Stream Join Processing." International Journal of Distributed Systems and Technologies 2, no. 2 (April 2011): 59–81. http://dx.doi.org/10.4018/jdst.2011040104.

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Abstract (sommario):
This paper presents an adaptive framework for processing a window-based multi-way join query over distributed data streams. The framework integrates distributed plan modification and distributed plan migration within the same scope by using a building block called the node operator set (NOS). An NOS is housed in each node that participates in the join execution, and specifies the set of atomic operations to be performed locally at the host node to execute its share of the global execution plan. The plan modification and migration techniques presented are for the case of updating the NOSs centralized at a single node and the case of updating them distributed at each node. The plan modification is triggered by the change of stream statistics and adjusts the join execution order and placement greedily to satisfy a cost invariant. The plan migration uses the distributed track strategy to accelerate the migration of window extents to new nodes. The migration of all window extents is synchronized. Experiments confirm the effectiveness of the developed adaptive framework on reducing the join execution cost and indicate a small additional adaptation-overhead for distributing the NOS update.
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33

Lederman, Susan J., Roger A. Browse, and Roberta L. Klatzky. "Haptic processing of spatially distributed information." Perception & Psychophysics 44, no. 3 (May 1988): 222–32. http://dx.doi.org/10.3758/bf03206291.

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34

Chen, Po-Chih, and Palghat Vaidyanathan. "Distributed Algorithms for Array Signal Processing." IEEE Transactions on Signal Processing 69 (2021): 4607–22. http://dx.doi.org/10.1109/tsp.2021.3101015.

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35

Darmont, Jérôme, Boris Novikov, Robert Wrembel, and Ladjel Bellatreche. "Data processing in modern distributed architectures." Information Systems 104 (February 2022): 101936. http://dx.doi.org/10.1016/j.is.2021.101936.

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36

Ras, Zbigniew W. "Query Processing in Distributed Information Systems." Fundamenta Informaticae 15, no. 3-4 (August 1, 1991): 381–97. http://dx.doi.org/10.3233/fi-1991-153-411.

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Abstract (sommario):
In our model of a distributed information system, two information structures are maintained – the application database and the (routing) database used to route queries in a computer network. Any site of a distributed information system which does not understand some attribute values used in a query has to search for a site which can explain them. The information stored in routing database has a strong impact on the speed of this search. In [5], we propose that each site learns from its neighbors the descriptions of all unknown attribute values used in queries. These descriptions are represented in the form of rules and stored in dictionaries added to all sites of a distributed information system. This extended distributed system with dictionaries is called intelligent.
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37

SrinivasaRao, P., V. P. C Rao, and A. Govardhan. "Priority based Distributed Job Processing System." International Journal of Computer Applications 60, no. 17 (December 18, 2012): 13–17. http://dx.doi.org/10.5120/9783-4319.

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38

Wiggins, R. E. "Distributed information processing: trends and implications." Aslib Proceedings 37, no. 2 (February 1985): 73–90. http://dx.doi.org/10.1108/eb050955.

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39

Yosuf, Barzan A., Mohamed Musa, Taisir Elgorashi, and Jaafar Elmirghani. "Energy Efficient Distributed Processing for IoT." IEEE Access 8 (2020): 161080–108. http://dx.doi.org/10.1109/access.2020.3020744.

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40

Tarasov, E. N. "Ergatic Distributed Data-Processing Systems Control." MEHATRONIKA, AVTOMATIZACIA, UPRAVLENIE 17, no. 4 (April 2016): 245–49. http://dx.doi.org/10.17587/mau.17.245-249.

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41

Stanoi, Ioana, George Mihaila, Themis Palpanas, and Christian Lang. "WhiteWater: Distributed Processing of Fast Streams." IEEE Transactions on Knowledge and Data Engineering 19, no. 9 (September 2007): 1214–26. http://dx.doi.org/10.1109/tkde.2007.1056.

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42

Bodorik, P., J. S. Riordon, and J. S. Pyra. "Deciding to correct distributed query processing." IEEE Transactions on Knowledge and Data Engineering 4, no. 3 (June 1992): 253–65. http://dx.doi.org/10.1109/69.142016.

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43

Bodorik, P., and J. S. Riordon. "Threshold Values for Processing Distributed Queries." Computer Journal 34, no. 6 (December 1, 1991): 551–58. http://dx.doi.org/10.1093/comjnl/34.6.551.

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44

Liu, C., and C. Yu. "Performance issues in distributed query processing." IEEE Transactions on Parallel and Distributed Systems 4, no. 8 (1993): 889–905. http://dx.doi.org/10.1109/71.238624.

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45

Banks, D. J., P. Degenaar, and C. Toumazou. "Distributed current-mode image processing filters." Electronics Letters 41, no. 22 (2005): 1201. http://dx.doi.org/10.1049/el:20052971.

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46

Baumgurtel, H. "Distributed constraint processing for production logistics." IEEE Intelligent Systems 15, no. 1 (January 2000): 40–48. http://dx.doi.org/10.1109/5254.820328.

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47

Banks, Richard, Peter Furniss, Klaus Heien, and H. Rüdiger Wiehle. "OSI distributed transaction processing commitment optimizations." ACM SIGCOMM Computer Communication Review 28, no. 5 (October 1998): 61–75. http://dx.doi.org/10.1145/303297.303308.

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48

Hochreiner, Christoph, Stefan Schulte, Schahram Dustdar, and Freddy Lecue. "Elastic Stream Processing for Distributed Environments." IEEE Internet Computing 19, no. 6 (November 2015): 54–59. http://dx.doi.org/10.1109/mic.2015.118.

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49

Smith, Jim, Paul Watson, Anastasios Gounaris, Norman W. Paton, Alvaro A. A. Fernandes, and Rizos Sakellariou. "Distributed Query Processing on the Grid." International Journal of High Performance Computing Applications 17, no. 4 (November 2003): 353–67. http://dx.doi.org/10.1177/10943420030174002.

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50

Gardner, Timothy J. "DPUP: a distributed processing utilities package." ACM SIGNUM Newsletter 21, no. 4 (November 1986): 5–19. http://dx.doi.org/10.1145/12492.12493.

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