De oliveira daniel c m (13 risultati)
Altre immaginiEl perfil de México en 1980. 3 Tomos. Tomo I: La Economía y la Población. Mercados, Desarrollo y Política Económica, El sistema financiero, La distribución del ingreso. Tomo II: Agricultura y Ganadería. Urbanización. Recursos Marinos y energéticos. Industria siderúrgica, automotriz y electrónica. Transporte y telecomunicaciones. Turismo. Educación. Problema habitacional. Tomo III. Sociología. Política. Cultura.
Ibarra, David; Ifigenia M. de Navarrete, Leopoldo Solís, Víctor L. Urquidi (Tomo I.). Manuel Bravo Jiménez, Gerardo M. Bueno, Arturo del Castillo, Daniel Díaz Díaz, Horacio Flores de la Peña, Enrique G. León López, Diego G. López Rosado, Rogelio Magar Vincent, Eugenio Méndez, Arnulfo Morales Amado, Daniel Ocampo Sigüenza, Jesús Puente Leyva, Manuel Rodríguez Cisneros, Luis Unikel (Tomo II). Jorge Basurto, Raúl Béjar Navarro, Ricardo Cinta G. Enrique Contrerars Suárez, Víctor m. Durán Ponte, Víctor Flores Olea, Julio Labastida Martín del Campo, Jorge Martínez Ríos, Humberto Muños García, Mario Ojeda Gómez, Orlandina de Oliveira, Luis de Pablo, José Calixto Rangel C; José Luis Reyna, Alberto Saracho, Leopoldo Solís M; Claudio Stern, Manuel
Lingua: Spagnolo
Editore: Siglo XXI Editores, 1972., México., 1972
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Da: Librería "Franz Kafka" México., Cuernavaca, MOR, MessicoLibrería "Franz Kafka" México.
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EUR 19,39
EUR 9,28 spedizioneSpedito da Messico a U.S.A.Quantità: 1 disponibili
Aggiungi al carrelloEncuadernación de tapa blanda. Condizione: Aceptable. Ibarra, David; Ifigenia M. de Navarrete, Leopoldo Solís, Víctor L. Urquidi (Tomo I.). Manuel Bravo Jiménez, Gerardo M. Bueno, Arturo del Castillo, Daniel Díaz Díaz, Horacio Flores de la Peña, Enrique G. León López, Diego G. López Rosado, Rogelio Magar Vincent, Eugenio Méndez,… Arnulfo Morales Amado, Daniel Ocampo Sigüenza, Jesús Puente Leyva, Manuel Rodríguez Cisneros, Luis Unikel (Tomo II). Jorge Basurto, Raúl Béjar Navarro, Ricardo Cinta G. Enrique Contrerars Suárez, Víctor m. Durán Ponte, Víctor Flores Olea, Julio Labastida Martín del Campo, Jorge Martínez Ríos, Humberto Muños García, Mario Ojeda Gómez, Orlandina de Oliveira, Luis de Pablo, José Calixto Rangel C; José Luis Reyna, Alberto Saracho, Leopoldo Solís M; Claudio Stern, Manuel Villa A, Luis Villoro (Tomo III). El perfil de México en 1980. 3 Tomos. Tomo I: La Economía y la Población. Mercados, Desarrollo y Política Económica, El sistema financiero, La distribución del ingreso. Tomo II: Agricultura y Ganadería. Urbanización. Recursos Marinos y energéticos. Industria siderúrgica, automotriz y electrónica. Transporte y telecomunicaciones. Turismo. Educación. Problema habitacional. Tomo III. Sociología. Política. Cultura. México, Siglo XXI Editores, 1972. Características: Rústica en buen estado. 199 p; 303 p; 624 p. (21 x 14 cms.). Peso: 700 grs. (3143 NVO).
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 84,12
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Condizione: New. 1st edition NO-PA16APR2015-KAP.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 73,46
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Condizione: New. In English.
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Da: Chiron Media, Wallingford, Regno UnitoChiron Media
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PF. Condizione: New.
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Da: Leopolis, Kraków, PoloniaLeopolis
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Soft cover. Condizione: New. 8vo (23.5 cm), XVII, 161 pp. Laminated wrappers. Synopsis: Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engineering. Such… workflows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc. Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data. In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engineering. Such work…flows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines.More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc.Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data.In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions.
Altre immagini- Brossura
Da: preigu, Osnabrück, Germaniapreigu
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EUR 59,40
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Taschenbuch. Condizione: Neu. Data-Intensive Workflow Management | Daniel C. M. de Oliveira (u. a.) | Taschenbuch | Synthesis Lectures on Data Management | xvii | Englisch | 2019 | Springer | EAN 9783031007446 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]…springer[dot]com | Anbieter: preigu.
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- Print on Demand
Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engine…ering. Such workflows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines.More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc.Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data.In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions. 180 pp. Englisch.
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 84,18
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Condizione: New. Print on Demand.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 86,54
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Condizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019
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Da: moluna, Greven, Germaniamoluna
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EUR 55,78
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astron…omy, and engineering. Such workflows u.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 64,19
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engineerin…g. Such workflows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines.More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc.Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data.In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 180 pp. Englisch.
