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Rozprawy doktorskie na temat „MAPREDUCE ARCHITECTURE”

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Data publikacji: 11 września 2023

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1

Trezzo, Christopher J. "Continuous MapReduce an architecture for large-scale in-situ data processing /." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/fullcit?p1477939.

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Thesis (M.S.)--University of California, San Diego, 2010.<br>Title from first page of PDF file (viewed July 16, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (leaves 48-51).
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Venumuddala, Ramu Reddy. "Distributed Frameworks Towards Building an Open Data Architecture." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801911/.

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Data is everywhere. The current Technological advancements in Digital, Social media and the ease at which the availability of different application services to interact with variety of systems are causing to generate tremendous volumes of data. Due to such varied services, Data format is now not restricted to only structure type like text but can generate unstructured content like social media data, videos and images etc. The generated Data is of no use unless been stored and analyzed to derive some Value. Traditional Database systems comes with limitations on the type of data format schema, a
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Kang, Seunghwa. "On the design of architecture-aware algorithms for emerging applications." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39503.

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This dissertation maps various kernels and applications to a spectrum of programming models and architectures and also presents architecture-aware algorithms for different systems. The kernels and applications discussed in this dissertation have widely varying computational characteristics. For example, we consider both dense numerical computations and sparse graph algorithms. This dissertation also covers emerging applications from image processing, complex network analysis, and computational biology. We map these problems to diverse multicore processors and manycore accelerators. We also us
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Ferreira, Leite Alessandro. "A user-centered and autonomic multi-cloud architecture for high performance computing applications." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112355/document.

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Le cloud computing a été considéré comme une option pour exécuter des applications de calcul haute performance. Bien que les plateformes traditionnelles de calcul haute performance telles que les grilles et les supercalculateurs offrent un environnement stable du point de vue des défaillances, des performances, et de la taille des ressources, le cloud computing offre des ressources à la demande, généralement avec des performances imprévisibles mais à des coûts financiers abordables. Pour surmonter les limites d’un cloud individuel, plusieurs clouds peuvent être combinés pour former une fédérat
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Elteir, Marwa Khamis. "A MapReduce Framework for Heterogeneous Computing Architectures." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28786.

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Nowadays, an increasing number of computational systems are equipped with heterogeneous compute resources, i.e., following different architecture. This applies to the level of a single chip, a single node and even supercomputers and large-scale clusters. With its impressive price-to-performance ratio as well as power efficiently compared to traditional multicore processors, graphics processing units (GPUs) has become an integrated part of these systems. GPUs deliver high peak performance; however efficiently exploiting their computational power requires the exploration of a multi-dimensional s
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Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.

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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Bi
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7

de, Souza Ferreira Tharso. "Improving Memory Hierarchy Performance on MapReduce Frameworks for Multi-Core Architectures." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/129468.

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La necesidad de analizar grandes conjuntos de datos de diferentes tipos de aplicaciones ha popularizado el uso de modelos de programación simplicados como MapReduce. La popularidad actual se justifica por ser una abstracción útil para expresar procesamiento paralelo de datos y también ocultar eficazmente la sincronización de datos, tolerancia a fallos y la gestión de balanceo de carga para el desarrollador de la aplicación. Frameworks MapReduce también han sido adaptados a los sistema multi-core y de memoria compartida. Estos frameworks proponen que cada core de una CPU ejecute una tarea Map
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Adornes, Daniel Couto. "A unified mapreduce programming interface for multi-core and distributed architectures." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2015. http://tede2.pucrs.br/tede2/handle/tede/6782.

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Submitted by Setor de Tratamento da Informa??o - BC/PUCRS (tede2@pucrs.br) on 2016-06-22T19:44:58Z No. of bitstreams: 1 DIS_DANIEL_COUTO_ADORNES_COMPLETO.pdf: 1894086 bytes, checksum: f87c59fa92f43ed62efaafd9c724ed8d (MD5)<br>Made available in DSpace on 2016-06-22T19:44:58Z (GMT). No. of bitstreams: 1 DIS_DANIEL_COUTO_ADORNES_COMPLETO.pdf: 1894086 bytes, checksum: f87c59fa92f43ed62efaafd9c724ed8d (MD5) Previous issue date: 2015-03-31<br>Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES<br>In order to improve performance, simplicity and scalability of large datasets processi
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9

Pan, Jie. "Modélisation et exécution des applications d'analyse de données multi-dimentionnelles sur architectures distribuées." Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00579125.

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Des quantités de données colossalles sont générées quotidiennement. Traiter de grands volumes de données devient alors un véritable challenge pour les logiciels d'analyse des données multidimensionnelles. De plus, le temps de réponse exigé par les utilisateurs de ces logiciels devient de plus en plus court, voire intéractif. Pour répondre à cette demande, une approche basée sur le calcul parallèle est une solution. Les approches traditionnelles reposent sur des architectures performantes, mais coûteuses, comme les super-calculateurs. D'autres architectures à faible coût sont également disponib
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Palanisamy, Balaji. "Cost-effective and privacy-conscious cloud service provisioning: architectures and algorithms." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52157.

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Cloud Computing represents a recent paradigm shift that enables users to share and remotely access high-powered computing resources (both infrastructure and software/services) contained in off-site data centers thereby allowing a more efficient use of hardware and software infrastructures. This growing trend in cloud computing, combined with the demands for Big Data and Big Data analytics, is driving the rapid evolution of datacenter technologies towards more cost-effective, consumer-driven, more privacy conscious and technology agnostic solutions. This dissertation is dedicated to taking a s
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Liu, Yu-Yang, and 劉育瑒. "Parallel Genetic-Fuzzy Mining with MapReduce Architecture." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/eq783m.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>103<br>Fuzzy data mining can successfully find out hidden linguistic association rules by transforming quantity information into fuzzy membership values. In the derivation process, good membership functions play a key role in achieving the quality of finial results. In the past, some researches were proposed to train membership functions by genetic algorithms and could indeed improve the quality of found rules. Those kinds of methods were, however, suffered from the long execution time in the training phase. Besides, after appropriate fuzzy membership functions are
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VARSHNEY, PRATEEK KUMAR VARSHNEY. "IMPLEMENTING PARALLEL PSO ALGORITHM USING MAPREDUCE ARCHITECTURE." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14678.

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ABSTRACT Optimization is the problem of finding minimum or maximum of a given objective function relative to some set, often representing a range of choices available in a certain situation. Particle Swarm Optimization (PSO) is a simple and effective evolutionary algorithm, but it may take a reasonable time to optimize complex objective functions which are deceptive or expensive. To avoid being trapped in local optima, Particle Swarm Optimization requires extensive exploration for multimodal and multidimensional functions. Expensive functions whose computational complexity may arise from depe
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LO, HSIANG-FU, and 羅祥福. "Study of Performance Optimization Scheme for Hadoop MapReduce Architecture." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/55720702234492522318.

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博士<br>國防大學理工學院<br>國防科學研究所<br>104<br>As the use of cloud computing increases rapidly, Big Data also continue to grow quickly. The performance of data processing for big data has become an important research issue. This thesis discusses performance measurement methods together with performance tuning scheme in Hadoop MapReduce and then correspondingly proposes the performance improvement methods. To design a performance measurement scheme for Hadoop information hiding applications, a Performance AnalysiS Scheme for MapReduce Information Hiding (PASS-MIH) model is proposed to analyze and measure
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14

Jun-YiHsu and 徐君毅. "A Job Scheduling of Fair Resource Allocation with Energy-Saving for MapReduce Architecture in Cloud Computing." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/44086073397709531365.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>100<br>Cloud computing is one of the most important technological and has a broad range of application. Hadoop is most commonly used in the cloud computing platform. The method of the task scheduler is fairly scheduling at hadoop. Fairly scheduling is easy to implement and distribute tasks fairly in the work environment, but has a multiplicative gap from optimal assignment. On the other hand, the method does not consider about the concept of energy saving. If adding the concept of energy saving in cloud computing, we will be able to gain some benefits. We map the
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15

Kendall, Wesley James. "A Scalable Architecture for Simplifying Full-Range Scientific Data Analysis." 2011. http://trace.tennessee.edu/utk_graddiss/1198.

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According to a recent exascale roadmap report, analysis will be the limiting factor in gaining insight from exascale data. Analysis problems that must operate on the full range of a dataset are among the most difficult. Some of the primary challenges in this regard come from disk access, data managment, and programmability of analysis tasks on exascale architectures. In this dissertation, I have provided an architectural approach that simplifies and scales data analysis on supercomputing architectures while masking parallel intricacies to the user. My architecture has three primary general con
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(9529172), Ejebagom J. Ojogbo. "ZipThru: A software architecture that exploits Zipfian skew in datasets for accelerating Big Data analysis." Thesis, 2020.

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<div>In the past decade, Big Data analysis has become a central part of many industries including entertainment, social networking, and online commerce. MapReduce, pioneered by Google, is a popular programming model for Big Data analysis, famous for its easy programmability due to automatic data partitioning, fault tolerance, and high performance. Majority of MapReduce workloads are summarizations, where the final output is a per-key ``reduced" version of the input, highlighting a shared property of each key in the input dataset.</div><div><br></div><div>While MapReduce was originally proposed
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