Literatura académica sobre el tema "Large Scale Processing"
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Artículos de revistas sobre el tema "Large Scale Processing"
Fulton, Scott P. "Large-scale processing of macromolecules". Current Opinion in Biotechnology 5, n.º 2 (abril de 1994): 201–5. http://dx.doi.org/10.1016/s0958-1669(05)80037-0.
Texto completoSiegel, Howard Jay, Thomas Schwederski, David G. Meyer y William Tsun-yuk Hsu. "Large-scale parallel processing systems". Microprocessors and Microsystems 11, n.º 1 (enero de 1987): 3–20. http://dx.doi.org/10.1016/0141-9331(87)90325-5.
Texto completoMIKI, Mitsunori. "Large-scale Simulation and Parallel Processing." Journal of the Society of Powder Technology, Japan 35, n.º 3 (1998): 192–97. http://dx.doi.org/10.4164/sptj.35.192.
Texto completoLee, Daewoo, Jin-Soo Kim y Seungryoul Maeng. "Large-scale incremental processing with MapReduce". Future Generation Computer Systems 36 (julio de 2014): 66–79. http://dx.doi.org/10.1016/j.future.2013.09.010.
Texto completoGanetsos, G. y P. E. Barker. "Large-scale chromatography in industrial processing". Journal of Chemical Technology & Biotechnology 50, n.º 1 (24 de abril de 2007): 101–8. http://dx.doi.org/10.1002/jctb.280500111.
Texto completoSterken, Yvonne, Alexander Toet y Yen-Lee Yap. "Factors Limiting Large-Scale Localisation". Perception 23, n.º 6 (junio de 1994): 709–26. http://dx.doi.org/10.1068/p230709.
Texto completoLiu, Ning, Dong-sheng Li, Yi-ming Zhang y Xiong-lve Li. "Large-scale graph processing systems: a survey". Frontiers of Information Technology & Electronic Engineering 21, n.º 3 (marzo de 2020): 384–404. http://dx.doi.org/10.1631/fitee.1900127.
Texto completoK¨ampf, Mirko y Jan W. Kantelhardt. "Hadoop. TS: Large-Scale Time-Series Processing". International Journal of Computer Applications 74, n.º 17 (26 de julio de 2013): 1–8. http://dx.doi.org/10.5120/12974-0233.
Texto completoKo, Seyoon y Joong-Ho Won. "Processing large-scale data with Apache Spark". Korean Journal of Applied Statistics 29, n.º 6 (31 de octubre de 2016): 1077–94. http://dx.doi.org/10.5351/kjas.2016.29.6.1077.
Texto completoJONES, ALEX K., DARREN J. KERBYSON, RAM RAJAMONY y CHARLES WEEMS. "GUEST EDITOR'S NOTE: LARGE-SCALE PARALLEL PROCESSING". Parallel Processing Letters 18, n.º 04 (diciembre de 2008): 449–51. http://dx.doi.org/10.1142/s0129626408003508.
Texto completoTesis sobre el tema "Large Scale Processing"
Kutlu, Mucahid. "Parallel Processing of Large Scale Genomic Data". The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436355132.
Texto completoCaneill, Matthieu. "Contributions to large-scale data processing systems". Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM006/document.
Texto completoThis thesis covers the topic of large-scale data processing systems,and more precisely three complementary approaches: the design of asystem to perform prediction about computer failures through theanalysis of monitoring data; the routing of data in a real-time systemlooking at correlations between message fields to favor locality; andfinally a novel framework to design data transformations usingdirected graphs of blocks.Through the lenses of the Smart Support Center project, we design ascalable architecture, to store time series reported by monitoringengines, which constantly check the health of computer systems. We usethis data to perform predictions, and detect potential problems beforethey arise.We then dive in routing algorithms for stream processing systems, anddevelop a layer to route messages more efficiently, by avoiding hopsbetween machines. For that purpose, we identify in real-time thecorrelations which appear in the fields of these messages, such ashashtags and their geolocation, for example in the case of tweets. Weuse these correlations to create routing tables which favor theco-location of actors handling these messages.Finally, we present λ-blocks, a novel programming framework to computedata processing jobs without writing code, but rather by creatinggraphs of blocks of code. The framework is fast, and comes withbatteries included: block libraries, plugins, and APIs to extendit. It is also able to manipulate computation graphs, foroptimization, analyzis, verification, or any other purposes
Wang, Liqiang. "An Efficient Platform for Large-Scale MapReduce Processing". ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/963.
Texto completoLarsson, Carl-Johan. "Movie Recommendation System Using Large Scale Graph-Processing". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.
Texto completoGardner, Tara Conti. "Delipidation Treatments for Large-Scale Protein Purification Processing". Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36512.
Texto completoMaster of Science
Wang, Jiayin. "Building Efficient Large-Scale Big Data Processing Platforms". Thesis, University of Massachusetts Boston, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10262281.
Texto completoIn the era of big data, many cluster platforms and resource management schemes are created to satisfy the increasing demands on processing a large volume of data. A general setting of big data processing jobs consists of multiple stages, and each stage represents generally defined data operation such as ltering and sorting. To parallelize the job execution in a cluster, each stage includes a number of identical tasks that can be concurrently launched at multiple servers. Practical clusters often involve hundreds or thousands of servers processing a large batch of jobs. Resource management, that manages cluster resource allocation and job execution, is extremely critical for the system performance.
Generally speaking, there are three main challenges in resource management of the new big data processing systems. First, while there are various pending tasks from dierent jobs and stages, it is difficult to determine which ones deserve the priority to obtain the resources for execution, considering the tasks' different characteristics such as resource demand and execution time. Second, there exists dependency among the tasks that can be concurrently running. For any two consecutive stages of a job, the output data of the former stage is the input data of the later one. The resource management has to comply with such dependency. The third challenge is the inconsistent performance of the cluster nodes. In practice, run-time performance of every server is varying. The resource management needs to dynamically adjust the resource allocation according to the performance change of each server.
The resource management in the existing platforms and prior work often rely on fixed user-specic congurations, and assumes consistent performance in each node. The performance, however, is not satisfactory under various workloads. This dissertation aims to explore new approaches to improving the eciency of large-scale big data processing platforms. In particular, the run-time dynamic factors are carefully considered when the system allocates the resources. New algorithms are developed to collect run-time data and predict the characteristics of jobs and the cluster. We further develop resource management schemes that dynamically tune the resource allocation for each stage of every running job in the cluster. New findings and techniques in this dissertation will certainly provide valuable and inspiring insights to other similar problems in the research community.
Clifford, Raphael. "Indexed strings for large scale genomic analysis". Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268368.
Texto completoSchaeppi, Reto. "Large scale processing of microarray data a diploma thesis /". Zurich : Information and Communication Systems Research Group, Institute of Information Systems, Swiss Federal Institute of Technology, 2002. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=48.
Texto completoMesmoudi, Amin. "Declarative parallel query processing on large scale astronomical databases". Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10326.
Texto completoThis work is carried out in framework of the PetaSky project. The objective of this project is to provide a set of tools allowing to manage Peta-bytes of data from astronomical observations. Our work is concerned with the design of a scalable approach. We first started by analyzing the ability of MapReduce based systems and supporting SQL to manage the LSST data and ensure optimization capabilities for certain types of queries. We analyzed the impact of data partitioning, indexing and compression on query performance. From our experiments, it follows that there is no “magic” technique to partition, store and index data but the efficiency of dedicated techniques depends mainly on the type of queries and the typology of data that are considered. Based on our work on benchmarking, we identified some techniques to be integrated to large-scale data management systems. We designed a new system allowing to support multiple partitioning mechanisms and several evaluation operators. We used the BSP (Bulk Synchronous Parallel) model as a parallel computation paradigm. Unlike MapeReduce model, we send intermediate results to workers that can continue their processing. Data is logically represented as a graph. The evaluation of queries is performed by exploring the data graph using forward and backward edges. We also offer a semi-automatic partitioning approach, i.e., we provide the system administrator with a set of tools allowing her/him to choose the manner of partitioning data using the schema of the database and domain knowledge. The first experiments show that our approach provides a significant performance improvement with respect to Map/Reduce systems
Dreibelbis, Harold N., Dennis Kelsch y Larry James. "REAL-TIME TELEMETRY DATA PROCESSING and LARGE SCALE PROCESSORS". International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612912.
Texto completoReal-time data processing of telemetry data has evolved from a highly centralized single large scale computer system to multiple mini-computers or super mini-computers tied together in a loosely coupled distributed network. Each mini-computer or super mini-computer essentially performing a single function in the real-time processing sequence of events. The reasons in the past for this evolution are many and varied. This paper will review some of the more significant factors in that evolution and will present some alternatives to a fully distributed mini-computer network that appear to offer significant real-time data processing advantages.
Libros sobre el tema "Large Scale Processing"
1960-, Morishita Shinichi, ed. Large-scale genome sequence processing. London: Imperial College Press, 2006.
Buscar texto completoWorkshop on Large-Scale Numerical Optimization (1989 Cornell University). Large-scale numerical optimization. Philadelphia: Society for Industrial and Applied Mathematics, 1990.
Buscar texto completoJ, Offen R., ed. VLSIimage processing. London: Collins, 1985.
Buscar texto completoSakr, Sherif, Faisal Moeen Orakzai, Ibrahim Abdelaziz y Zuhair Khayyat. Large-Scale Graph Processing Using Apache Giraph. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47431-1.
Texto completoCecil, J. E. Small, medium and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.
Buscar texto completoCecil, J. E. Small, medium and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.
Buscar texto completoApplication architecture: Modern large-scale information processing. New York: Wiley, 1990.
Buscar texto completoCecil, J. E. Small-, medium-, and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.
Buscar texto completoJ, Offen R., ed. VLSI image processing. London: Collins, 1985.
Buscar texto completoLarge scale and big data: Processing and management. Boca Raton: Taylor & Francis, 2014.
Buscar texto completoCapítulos de libros sobre el tema "Large Scale Processing"
Atanassov, E. I., D. Georgiev, T. Gurov, A. Karaivanova y Y. Nikolova. "Distributed System for Query Processing with Grid Authentication". En Large-Scale Scientific Computing, 467–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43880-0_53.
Texto completoSakr, Sherif. "Large-Scale Graph Processing Systems". En Big Data 2.0 Processing Systems, 59–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44187-6_4.
Texto completoSakr, Sherif. "Large-Scale Stream Processing Systems". En Big Data 2.0 Processing Systems, 95–115. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44187-6_5.
Texto completovan der Stockt, Stefan, Aaron K. Baughman y Michael Perlitz. "Large-Scale Biometric Multimedia Processing". En Multimedia Data Mining and Analytics, 177–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_8.
Texto completoSakr, Sherif. "Large-Scale Graph Processing Systems". En Big Data 2.0 Processing Systems, 53–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38776-5_4.
Texto completoSakr, Sherif. "Large-Scale Stream Processing Systems". En Big Data 2.0 Processing Systems, 75–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38776-5_5.
Texto completoNagel, Kai, Marcus Rickert y Christopher L. Barrett. "Large scale traffic simulations". En Vector and Parallel Processing — VECPAR'96, 380–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62828-2_131.
Texto completoDechevsky, Lubomir T., Børre Bang, Joakim Gundersen, Arne Lakså y Arnt R. Kristoffersen. "Solving Non-linear Systems of Equations on Graphics Processing Units". En Large-Scale Scientific Computing, 719–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_86.
Texto completoDechevsky, Lubomir, Joakim Gundersen y Børre Bang. "Computing n-Variate Orthogonal Discrete Wavelet Transforms on Graphics Processing Units". En Large-Scale Scientific Computing, 730–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_87.
Texto completoZhou, Rong y Liqing Zhang. "Contour-Based Large Scale Image Retrieval". En Neural Information Processing, 565–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_64.
Texto completoActas de conferencias sobre el tema "Large Scale Processing"
Chen, Kang, Yubing Yin y Weimin Zheng. "Teaching large scale data processing". En the 1st ACM Summit on Computing Education in China. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1517632.1517635.
Texto completoMargan, Domagoj y Peter Pietzuch. "Large-Scale Stream Graph Processing". En DEBS '17: The 11th ACM International Conference on Distributed and Event-based Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3093742.3093907.
Texto completoVerstoep, Kees, Henri E. Bal, Jiri Barnat y Lubos Brim. "Efficient large-scale model checking". En Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161000.
Texto completoLiu, Alex X., Ke Shen y Eric Torng. "Large scale Hamming distance query processing". En 2011 IEEE International Conference on Data Engineering (ICDE 2011). IEEE, 2011. http://dx.doi.org/10.1109/icde.2011.5767831.
Texto completovan der Zant, Tijn, Lambert Schomaker y Edwin Valentijn. "Large scale parallel document image processing". En Electronic Imaging 2008, editado por Berrin A. Yanikoglu y Kathrin Berkner. SPIE, 2008. http://dx.doi.org/10.1117/12.765482.
Texto completoHeindl, Christoph, Gernot Stübl, Thomas Pönitz, Andreas Pichler y Josef Scharinger. "Visual large-scale industrial interaction processing". En UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341162.3343769.
Texto completoIm, ChangJin, Jae-Heon Jeong y Chang-Sung Jeong. "Parallel Large-Scale Image Processing for Orthorectification". En TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650289.
Texto completoPernpeintner, Reinhold, Angelika Fröhlich y Thomas Lippert. "CFRP Infusion Processing on Large-Scale Cylindrica..." En 56th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.iac-05-c2.1.a.04.
Texto completoHerbst, Ludolf y Jan Brune. "Micro-scale large-area UV laser processing". En SPIE LASE: Lasers and Applications in Science and Engineering, editado por Wilhelm Pfleging, Yongfeng Lu, Kunihiko Washio, Willem Hoving y Jun Amako. SPIE, 2009. http://dx.doi.org/10.1117/12.807828.
Texto completoFilho, P. Souza, L. Felipe, P. Aragäo, L. Bejarano, D. Thomé de Paula, A. Sardinha, A. Azambuja y F. Sierra. "Large Scale Seismic Processing in Public Cloud". En 82nd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202011916.
Texto completoInformes sobre el tema "Large Scale Processing"
Popek, Gerald J. y Wesley W. Chu. Very Large Scale Distributed Information Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1991. http://dx.doi.org/10.21236/ada243983.
Texto completoWhiting, M. A. Object-oriented design: Deriving conceptual solutions to large-scale information processing problems. Office of Scientific and Technical Information (OSTI), mayo de 1990. http://dx.doi.org/10.2172/6895465.
Texto completoBilgutay, Nihat M. Computer Facilities for High-Speed Data Acquisition, Signal Processing and Large Scale System Simulation. Fort Belvoir, VA: Defense Technical Information Center, junio de 1986. http://dx.doi.org/10.21236/ada170935.
Texto completoGaliani, Sebastian, Ramiro Gálvez y Ian Nachman. Unveiling Specialization Trends in Economics Research: A Large-Scale Study Using Natural Language Processing and Citation Analysis. Cambridge, MA: National Bureau of Economic Research, junio de 2023. http://dx.doi.org/10.3386/w31295.
Texto completoHenz, Brian J., John Lazorisak, Jaroslaw Knap, Jason Livingston y Dale R. Shires. Installation to Production of a Large-Scale General Purpose Graphics Processing Unit (GPGPU) Cluster at the U.S. Army Research Laboratory: Thufir. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2014. http://dx.doi.org/10.21236/ada610234.
Texto completoHobbs, D. T., D. Chai, D. Hartsough y D. Genders. Final Report on the Large Scale Demonstration for the Electrochemical Processing of Hanford and Savannah River Site High-Level Waste Simulants. Office of Scientific and Technical Information (OSTI), septiembre de 1995. http://dx.doi.org/10.2172/607121.
Texto completoSalter, R., Quyen Dong, Cody Coleman, Maria Seale, Alicia Ruvinsky, LaKenya Walker y W. Bond. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), abril de 2021. http://dx.doi.org/10.21079/11681/40203.
Texto completoThompson. L52208 Coating and Backfill System Optimisation. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), mayo de 2004. http://dx.doi.org/10.55274/r0010964.
Texto completoSIEGFRIED, MATTHEW, WILLIAM RAMSEY y MATTHEW WILLIAMS. PERMANGANATE OXIDATION OF DEFENSE WASTE PROCESSING FACILITY (DWPF) RECYCLE COLLECTION TANK (RCT) SIMULANTS LARGER SCALE PROTOCOL RUNS - CHEMICAL PROCESS CELL (CPC) NOMINAL AND FOAMOVER CONDITIONS. Office of Scientific and Technical Information (OSTI), noviembre de 2019. http://dx.doi.org/10.2172/1701701.
Texto completoEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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