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Relevant bibliographies by topics / Distributed processing

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Academic literature on the topic 'Distributed processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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Journal articles on the topic "Distributed processing"

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

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

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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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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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Dissertations / Theses on the topic "Distributed processing"

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Lee, Li 1975. "Distributed signal processing." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86436.

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Lu, Yu-En. "Distributed proximity query processing." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612165.

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Wu, Tsung-li. "Distributed processing on link enhancement." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23869.

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de, Errico Luciano. "Agent-based distributed parallel processing." Thesis, University of Surrey, 1996. http://epubs.surrey.ac.uk/843822/.

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This work concerns the design and prototype implementation of an agent-based parallel architecture for physically distributed systems. The generic goal is to combine the processing power of widely available, low-cost networks of workstations, providing parallelism inside single applications. The specific goal is to investigate ways of implementing agent-based parallel processing in distributed systems. In this context, an agent is a lightweight mobile process that can freely move in the network and execute when it reaches a processing node. The Swarm architecture addresses these points by providing an abstract environment that can span many or all machines in the network. The environment is structured as a virtual machine, whose organisation and instruction set are detailed. Swarm is based on the idea of process flow, in which mobile concurrent processes can move and execute asynchronously in a distributed space consisting of data nodes. Each node is capable of permanently storing arbitrary information and references to other nodes, permitting the creation of persistent and distributed data structures in the environment. The main advantage is a flexible programming environment, which combines characteristics of the message-passing and distributed shared-memory approaches. A subset of the Swarm architecture was implemented as a prototype, coded in C language for operation under the Unix environment, to study and evaluate the model. The prototype executed in a single workstation, simulating the Swarm abstract environment and pennitting the validation of the proposed architecture and implemented mechanisms. Both the implementation and the evaluation procedure are explained and discussed. Results suggest that agent-based processing is feasible in moderately-and tightly-coupled environments, and that the Swarm processing model can be successfully applied to local-area networks and massively parallel computing machines. In particular, applications that manipulate irregular and distributed data structures can benefit from the programming environment provided by the Swarm architecture. These comprise: symbolic processing (artificial intelligence and expert systems), distributed simulation, distributed databases, and intelligent networks.

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Norcross, Stuart John. "Deriving distributed garbage collectors from distributed termination algorithms." Thesis, University of St Andrews, 2004. http://hdl.handle.net/10023/14986.

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This thesis concentrates on the derivation of a modularised version of the DMOS distributed garbage collection algorithm and the implementation of this algorithm in a distributed computational environment. DMOS appears to exhibit a unique combination of attractive characteristics for a distributed garbage collector but the original algorithm is known to contain a bug and, previous to this work, lacks a satisfactory, understandable implementation. The relationship between distributed termination detection algorithms and distributed garbage collectors is central to this thesis. A modularised DMOS algorithm is developed using a previously published distributed garbage collector derivation methodology that centres on mapping centralised collection schemes to distributed termination detection algorithms. In examining the utility and suitability of the derivation methodology, a family of six distributed collectors is developed and an extension to the methodology is presented. The research work described in this thesis incorporates the definition and implementation of a distributed computational environment based on the ProcessBase language and a generic definition of a previously unimplemented distributed termination detection algorithm called Task Balancing. The role of distributed termination detection in the DMOS collection mechanisms is defined through a process of step-wise refinement. The implementation of the collector is achieved in two stages; the first stage defines the implementation of two distributed termination mappings with the Task Balancing algorithm; the second stage defines the DMOS collection mechanisms.

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Benelallam, Amine. "Model transformation on distributed platforms : decentralized persistence and distributed processing." Thesis, Nantes, Ecole des Mines, 2016. http://www.theses.fr/2016EMNA0288/document.

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Grâce à sa promesse de réduire les efforts de développement et maintenance du logiciel, l’Ingénierie Dirigée par les Modèles (IDM) attire de plus en plus les acteurs industriels. En effet, elle a été adoptée avec succès dans plusieurs domaines tels que le génie civil, l’industrie automobile et la modernisation de logiciels.Toutefois, la taille croissante des modèles utilisés nécessite de concevoir des solutions passant à l’échelle afin de les traiter (transformer), et stocker (persister) de manière efficace. Une façon de pallier cette problématique est d’utiliser les systèmes et les bases de données répartis. D’une part, les paradigmes de programmation distribuée tels que MapReduce et Pregel peuvent simplifier la distribution de transformations des modèles (TM). Et d’autre part, l’avènement des base de données NoSQL permet le stockage efficace des modèles d’une manière distribuée. Dans le cadre de cette thèse, nous proposons une approche pour la transformation ainsi que pour la persistance de grands modèles.Nous nous basons d’un côté, sur le haut niveau d’abstraction fourni par les langages déclaratifs (relationnels) de transformation et d’un autre côté, sur la sémantique bien définie des paradigmes existants de programmation distribués, afin de livrer un moteur distribué de TM. La distribution est implicite et la syntaxe du langage n’est pas modifiée (aucune primitive de parallélisation n’est ajoutée). Nous étendons cette solution avec un algorithme efficace de distribution de modèles qui se base sur l’analyse statique des transformations et sur résultats récents sur le partitionnement équilibré des graphes. Nous avons appliqué notre approche à ATL, un langage relationnel de TM et MapReduce, un paradigme de programmation distribué. Finalement, nous proposons une solution pour stocker des modèles à l’aide de bases de données NoSQL, en particulier au travers d’un cadre d’applications de persistance répartie
Model-Driven Engineering (MDE) is gaining ground in industrial environments, thanks to its promise of lowering software development and maintenance effort. It has been adopted with success in producing software for several domains like civil engineering, car manufacturing and modernization of legacy software systems. As the models that need to be handled in model-driven engineering grow in scale, it became necessary to design scalable algorithms for model transformation (MT) as well as well-suitable persistence frameworks. One way to cope with these issues is to exploit the wide availability of distributed clusters in the Cloud for the distributed execution of model transformations and their persistence. On one hand, programming models such as MapReduce and Pregel may simplify the development of distributed model transformations. On the other hand, the availability of different categories of NoSQL databases may help to store efficiently the models. However, because of the dense interconnectivity of models and the complexity of transformation logics, scalability in distributed model processing is challenging. In this thesis, we propose our approach for scalable model transformation and persistence. We exploit the high-level of abstraction of relational MT languages and the well-defined semantics of existing distributed programming models to provide a relational model transformation engine with implicit distributed execution. The syntax of the MT language is not modified and no primitive for distribution is added. Hence developers are not required to have any acquaintance with distributed programming.We extend this approach with an efficient model distribution algorithm, based on the analysis of relational model transformation and recent results on balanced partitioning of streaming graphs. We applied our approach to a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. Finally, we propose a multi-persistence backend for manipulating and storing models in NoSQL databases according to the modeling scenario. Especially, we focus on decentralized model persistence for distributed model transformations

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孫昱東 and Yudong Sun. "A distributed object model for solving irregularly structured problemson distributed systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31243630.

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Kumar, Rohit 1986. "Temporal graph mining and distributed processing." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/620623.

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With the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamics of the network. Understanding the dynamics of the network is very important because it encapsulates the way we communicate, socialize, consume information and get influenced. To this end, in this PhD thesis, we focus on analyzing an interaction network to understand how the underlying network is being used. We define interaction network as a sequence of time-stamped interactions E over edges of a static graph G=(V, E). Interaction networks can be used to model many real-world networks for example, in a social network or a communication network, each interaction over an edge represents an interaction between two users, e.g., emailing, making a call, re-tweeting, or in case of the financial network an interaction between two accounts to represent a transaction. We analyze interaction network under two settings. In the first setting, we study interaction network under a sliding window model. We assume a node could pass information to other nodes if they are connected to them using edges present in a time window. In this model, we study how the importance or centrality of a node evolves over time. In the second setting, we put additional constraints on how information flows between nodes. We assume a node could pass information to other nodes only if there is a temporal path between them. To restrict the length of the temporal paths we consider a time window in this approach as well. We apply this model to solve the time-constrained influence maximization problem. By analyzing the interaction network data under our model we find the top-k most influential nodes. We test our model both on human-human interaction using social network data as well as on location-location interaction using location-based social network(LBSNs) data. In the same setting, we also mine temporal cyclic paths to understand the communication patterns in a network. Temporal cycles have many applications and appear naturally in communication networks where one person posts a message and after a while reacts to a thread of reactions from peers on the post. In financial networks, on the other hand, the presence of a temporal cycle could be indicative of certain types of fraud. We provide efficient algorithms for all our analysis and test their efficiency and effectiveness on real-world data. Finally, given that many of the algorithms we study have huge computational demands, we also studied distributed graph processing algorithms. An important aspect of distributed graph processing is to correctly partition the graph data between different machine. A lot of research has been done on efficient graph partitioning strategies but there is no one good partitioning strategy for all kind of graphs and algorithms. Choosing the best partitioning strategy is nontrivial and is mostly a trial and error exercise. To address this problem we provide a cost model based approach to give a better understanding of how a given partitioning strategy is performing for a given graph and algorithm.
Con el reciente crecimiento de las redes sociales y el deseo humano de interactuar con el mundo digital, una gran cantidad de datos de interacción humano-a-humano o humano-a-dispositivo se generan cada segundo. Con el auge de los dispositivos IoT, las interacciones dispositivo-a-dispositivo también están en alza. Todas estas interacciones no son más que una representación de como la red subyacente conecta distintas entidades en el tiempo. Modelar estas interacciones en forma de red de interacciones presenta una gran cantidad de oportunidades únicas para descubrir patrones interesantes y entender la dinamicidad de la red. Entender la dinamicidad de la red es clave ya que encapsula la forma en la que nos comunicamos, socializamos, consumimos información y somos influenciados. Para ello, en esta tesis doctoral, nos centramos en analizar una red de interacciones para entender como la red subyacente es usada. Definimos una red de interacciones como una sequencia de interacciones grabadas en el tiempo E sobre aristas de un grafo estático G=(V, E). Las redes de interacción se pueden usar para modelar gran cantidad de aplicaciones reales, por ejemplo en una red social o de comunicaciones cada interacción sobre una arista representa una interacción entre dos usuarios (correo electrónico, llamada, retweet), o en el caso de una red financiera una interacción entre dos cuentas para representar una transacción. Analizamos las redes de interacción bajo múltiples escenarios. En el primero, estudiamos las redes de interacción bajo un modelo de ventana deslizante. Asumimos que un nodo puede mandar información a otros nodos si estan conectados utilizando aristas presentes en una ventana temporal. En este modelo, estudiamos como la importancia o centralidad de un nodo evoluciona en el tiempo. En el segundo escenario añadimos restricciones adicionales respecto como la información fluye entre nodos. Asumimos que un nodo puede mandar información a otros nodos solo si existe un camino temporal entre ellos. Para restringir la longitud de los caminos temporales también asumimos una ventana temporal. Aplicamos este modelo para resolver este problema de maximización de influencia restringido temporalmente. Analizando los datos de la red de interacción bajo nuestro modelo intentamos descubrir los k nodos más influyentes. Examinamos nuestro modelo en interacciones humano-a-humano, usando datos de redes sociales, como en ubicación-a-ubicación usando datos de redes sociales basades en localización (LBSNs). En el mismo escenario también minamos camínos cíclicos temporales para entender los patrones de comunicación en una red. Existen múltiples aplicaciones para cíclos temporales y aparecen naturalmente en redes de comunicación donde una persona envía un mensaje y después de un tiempo reacciona a una cadena de reacciones de compañeros en el mensaje. En redes financieras, por otro lado, la presencia de un ciclo temporal puede indicar ciertos tipos de fraude. Proponemos algoritmos eficientes para todos nuestros análisis y evaluamos su eficiencia y efectividad en datos reales. Finalmente, dado que muchos de los algoritmos estudiados tienen una gran demanda computacional, también estudiamos los algoritmos de procesado distribuido de grafos. Un aspecto importante de procesado distribuido de grafos es el de correctamente particionar los datos del grafo entre distintas máquinas. Gran cantidad de investigación se ha realizado en estrategias para particionar eficientemente un grafo, pero no existe un particionamento bueno para todos los tipos de grafos y algoritmos. Escoger la mejor estrategia de partición no es trivial y es mayoritariamente un ejercicio de prueba y error. Con tal de abordar este problema, proporcionamos un modelo de costes para dar un mejor entendimiento en como una estrategia de particionamiento actúa dado un grafo y un algoritmo.

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Lei, Ma. "Distributed query processing using composite semijoins." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ62238.pdf.

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Liu, Ying. "Query optimization for distributed stream processing." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3274258.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2007.
Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4597. Adviser: Beth Plale. Title from dissertation home page (viewed Apr. 21, 2008).

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Books on the topic "Distributed processing"

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Brooke, Phillip J., and Richard F. Paige. Practical Distributed Processing. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-841-8.

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Raymond, Kerry, and Liz Armstrong, eds. Open Distributed Processing. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-0-387-34882-7.

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Rumelhart, David. Parallel distributed processing. Piscateway, NJ: Institute of Electrical and Electronics Engineers, 1988.

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Rolia, Jerome, Jacob Slonim, and John Botsford, eds. Open Distributed Processing and Distributed Platforms. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35188-9.

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

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Engineering, University of Sheffield Department of Automatic Control and Systems. Parallel processing & distributed systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1992.

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Rolim, José, Frank Mueller, Albert Y. Zomaya, Fikret Ercal, Stephan Olariu, Binoy Ravindran, Jan Gustafsson, et al., eds. Parallel and Distributed Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0097882.

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Rolim, José, ed. Parallel and Distributed Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45591-4.

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Rolim, José, ed. Parallel and Distributed Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64359-1.

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W, Chu Wesley, ed. Distributed systems. Dedham, MA: Artech House, 1986.

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Book chapters on the topic "Distributed processing"

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Buchanan, W. J. "Distributed processing." In The Complete Handbook of the Internet, 79–106. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-306-48331-8_5.

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Beynon-Davies, Paul. "Distributed Processing." In Database Systems, 477–85. London: Macmillan Education UK, 2004. http://dx.doi.org/10.1007/978-0-230-00107-7_36.

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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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Weik, Martin H. "distributed processing." In Computer Science and Communications Dictionary, 444. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5403.

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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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Sattler, Kai-Uwe. "Distributed Query Processing." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_704-2.

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Sattler, Kai-Uwe. "Distributed Query Processing." In Encyclopedia of Database Systems, 912–17. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_704.

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Pettifer, Steve R., and Teresa K. Attwood. "Distributed Query Processing." In Encyclopedia of Systems Biology, 604–5. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1373.

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Rai, Rebika. "Distributed Transaction Processing." In NoSQL: Database for Storage and Retrieval of Data in Cloud, 1–22. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2016] |Includes bibliographical references and index.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315155579-2.

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Özsu, M. Tamer, and Patrick Valduriez. "Distributed Query Processing." In Principles of Distributed Database Systems, 129–82. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26253-2_4.

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Conference papers on the topic "Distributed processing"

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DePesa, Paul, and Danny Keogan. "Distributed hierarchical processing." In Photomask and Next Generation Lithography Mask Technology IX, edited by Hiroichi Kawahira. SPIE, 2002. http://dx.doi.org/10.1117/12.476932.

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Carlini, Emanuele, Patrizio Dazzi, Alessandro Lulli, and Laura Ricci. "Distributed graph processing." In SAC 2016: Symposium on Applied Computing. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2851613.2851746.

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Hose, Katja, and Akrivi Vlachou. "Distributed skyline processing." In the 15th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2247596.2247665.

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Nierstrasz, Oscar, Alan Snyder, Anthony S. Williams, and William Cook. "Open distributed processing (panel)." In Addendum to the proceedings. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/260303.260322.

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Chuan Lei, E. A. Rundensteiner, and J. D. Guttman. "Robust distributed stream processing." In 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544877.

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Slusallek, Philipp, Peter Shirley, William Mark, Gordon Stoll, and Ingo Wald. "Parallel & distributed processing." In ACM SIGGRAPH 2005 Courses. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1198555.1198750.

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Butera, William, V. Michael Bove, Jr., and James McBride. "Extremely distributed media processing." In Electronic Imaging 2002, edited by Sethuraman Panchanathan, V. Michael Bove, Jr., and Subramania I. Sudharsanan. SPIE, 2001. http://dx.doi.org/10.1117/12.451075.

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"Session: distributed information processing." In 1988 IEEE International Symposium on Information Theory. IEEE, 1988. http://dx.doi.org/10.1109/isit.1988.22296.

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Merticariu, Vlad, and Peter Baumann. "Massively Distributed Datacube Processing." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8900432.

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Schilling, Björn, Boris Koldehofe, Udo Pletat, and Kurt Rothermel. "Distributed heterogeneous event processing." In the Fourth ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1827418.1827453.

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Reports on the topic "Distributed processing"

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Tong, Lang. Network-Centric Distributed Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada519513.

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Gardner, Timothy J., Isabelle M. Gerard, Carla R. Mowers, Evi Nemeth, and Robert B. Schnabel. DPUP: A Distributed Processing Utilities Package. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada456864.

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Victor, R. A., P. J. Farris, and V. Maston. Distributed generation - the fuel processing example. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/460269.

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Pritchett, William C. Distributed Processing Using Single-chip Microcomputers. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada375765.

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Raghavendra, Cauligi S., and Viktor K. Prasanna. Distributed Signal Processing in Wireless Sensor Networks. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada437824.

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Brady, David J. Distributed Optoelectronic Processing of Multidimensional Digital Imaging. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada406120.

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Friedlander, Benjamin. Array Processing for Discrete and Distributed Sources. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada428940.

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Lavery, John. Distributed Microsensing: Devices Networks and Information Processing. Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada420760.

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Popek, Gerald J., and Wesley W. Chu. Very Large Scale Distributed Information Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada243983.

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Moura, Jose M. Distributed Sensing and Processing: A Graphical Model Approach. Fort Belvoir, VA: Defense Technical Information Center, November 2005. http://dx.doi.org/10.21236/ada455686.

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