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Статті в журналах з теми "Large Scale Processing"
Fulton, Scott P. "Large-scale processing of macromolecules." Current Opinion in Biotechnology 5, no. 2 (April 1994): 201–5. http://dx.doi.org/10.1016/s0958-1669(05)80037-0.
Повний текст джерелаSiegel, Howard Jay, Thomas Schwederski, David G. Meyer, and William Tsun-yuk Hsu. "Large-scale parallel processing systems." Microprocessors and Microsystems 11, no. 1 (January 1987): 3–20. http://dx.doi.org/10.1016/0141-9331(87)90325-5.
Повний текст джерелаMIKI, Mitsunori. "Large-scale Simulation and Parallel Processing." Journal of the Society of Powder Technology, Japan 35, no. 3 (1998): 192–97. http://dx.doi.org/10.4164/sptj.35.192.
Повний текст джерелаLee, Daewoo, Jin-Soo Kim, and Seungryoul Maeng. "Large-scale incremental processing with MapReduce." Future Generation Computer Systems 36 (July 2014): 66–79. http://dx.doi.org/10.1016/j.future.2013.09.010.
Повний текст джерелаGanetsos, G., and P. E. Barker. "Large-scale chromatography in industrial processing." Journal of Chemical Technology & Biotechnology 50, no. 1 (April 24, 2007): 101–8. http://dx.doi.org/10.1002/jctb.280500111.
Повний текст джерелаSterken, Yvonne, Alexander Toet, and Yen-Lee Yap. "Factors Limiting Large-Scale Localisation." Perception 23, no. 6 (June 1994): 709–26. http://dx.doi.org/10.1068/p230709.
Повний текст джерелаLiu, Ning, Dong-sheng Li, Yi-ming Zhang, and Xiong-lve Li. "Large-scale graph processing systems: a survey." Frontiers of Information Technology & Electronic Engineering 21, no. 3 (March 2020): 384–404. http://dx.doi.org/10.1631/fitee.1900127.
Повний текст джерелаK¨ampf, Mirko, and Jan W. Kantelhardt. "Hadoop. TS: Large-Scale Time-Series Processing." International Journal of Computer Applications 74, no. 17 (July 26, 2013): 1–8. http://dx.doi.org/10.5120/12974-0233.
Повний текст джерелаKo, Seyoon, and Joong-Ho Won. "Processing large-scale data with Apache Spark." Korean Journal of Applied Statistics 29, no. 6 (October 31, 2016): 1077–94. http://dx.doi.org/10.5351/kjas.2016.29.6.1077.
Повний текст джерелаJONES, ALEX K., DARREN J. KERBYSON, RAM RAJAMONY, and CHARLES WEEMS. "GUEST EDITOR'S NOTE: LARGE-SCALE PARALLEL PROCESSING." Parallel Processing Letters 18, no. 04 (December 2008): 449–51. http://dx.doi.org/10.1142/s0129626408003508.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаCaneill, Matthieu. "Contributions to large-scale data processing systems." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM006/document.
Повний текст джерелаThis 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.
Повний текст джерелаLarsson, 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.
Повний текст джерелаGardner, Tara Conti. "Delipidation Treatments for Large-Scale Protein Purification Processing." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36512.
Повний текст джерелаMaster 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.
Повний текст джерелаIn 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.
Повний текст джерелаSchaeppi, 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.
Повний текст джерелаMesmoudi, Amin. "Declarative parallel query processing on large scale astronomical databases." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10326.
Повний текст джерелаThis 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, and Larry James. "REAL-TIME TELEMETRY DATA PROCESSING and LARGE SCALE PROCESSORS." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612912.
Повний текст джерелаReal-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.
Книги з теми "Large Scale Processing"
1960-, Morishita Shinichi, ed. Large-scale genome sequence processing. London: Imperial College Press, 2006.
Знайти повний текст джерелаWorkshop on Large-Scale Numerical Optimization (1989 Cornell University). Large-scale numerical optimization. Philadelphia: Society for Industrial and Applied Mathematics, 1990.
Знайти повний текст джерелаJ, Offen R., ed. VLSIimage processing. London: Collins, 1985.
Знайти повний текст джерелаSakr, Sherif, Faisal Moeen Orakzai, Ibrahim Abdelaziz, and 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.
Повний текст джерелаCecil, J. E. Small, medium and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.
Знайти повний текст джерелаCecil, J. E. Small, medium and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.
Знайти повний текст джерелаApplication architecture: Modern large-scale information processing. New York: Wiley, 1990.
Знайти повний текст джерелаCecil, J. E. Small-, medium-, and large-scale starch processing. Rome: Food and Agriculture Organization of the United Nations, 1992.
Знайти повний текст джерелаJ, Offen R., ed. VLSI image processing. London: Collins, 1985.
Знайти повний текст джерелаLarge scale and big data: Processing and management. Boca Raton: Taylor & Francis, 2014.
Знайти повний текст джерелаЧастини книг з теми "Large Scale Processing"
Atanassov, E. I., D. Georgiev, T. Gurov, A. Karaivanova, and Y. Nikolova. "Distributed System for Query Processing with Grid Authentication." In Large-Scale Scientific Computing, 467–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43880-0_53.
Повний текст джерелаSakr, Sherif. "Large-Scale Graph Processing Systems." In 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.
Повний текст джерелаSakr, Sherif. "Large-Scale Stream Processing Systems." In 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.
Повний текст джерелаvan der Stockt, Stefan, Aaron K. Baughman, and Michael Perlitz. "Large-Scale Biometric Multimedia Processing." In Multimedia Data Mining and Analytics, 177–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_8.
Повний текст джерелаSakr, Sherif. "Large-Scale Graph Processing Systems." In 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.
Повний текст джерелаSakr, Sherif. "Large-Scale Stream Processing Systems." In 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.
Повний текст джерелаNagel, Kai, Marcus Rickert, and Christopher L. Barrett. "Large scale traffic simulations." In 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.
Повний текст джерелаDechevsky, Lubomir T., Børre Bang, Joakim Gundersen, Arne Lakså, and Arnt R. Kristoffersen. "Solving Non-linear Systems of Equations on Graphics Processing Units." In Large-Scale Scientific Computing, 719–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_86.
Повний текст джерелаDechevsky, Lubomir, Joakim Gundersen, and Børre Bang. "Computing n-Variate Orthogonal Discrete Wavelet Transforms on Graphics Processing Units." In Large-Scale Scientific Computing, 730–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_87.
Повний текст джерелаZhou, Rong, and Liqing Zhang. "Contour-Based Large Scale Image Retrieval." In Neural Information Processing, 565–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_64.
Повний текст джерелаТези доповідей конференцій з теми "Large Scale Processing"
Chen, Kang, Yubing Yin, and Weimin Zheng. "Teaching large scale data processing." In 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.
Повний текст джерелаMargan, Domagoj, and Peter Pietzuch. "Large-Scale Stream Graph Processing." In 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.
Повний текст джерелаVerstoep, Kees, Henri E. Bal, Jiri Barnat, and Lubos Brim. "Efficient large-scale model checking." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161000.
Повний текст джерелаLiu, Alex X., Ke Shen, and Eric Torng. "Large scale Hamming distance query processing." In 2011 IEEE International Conference on Data Engineering (ICDE 2011). IEEE, 2011. http://dx.doi.org/10.1109/icde.2011.5767831.
Повний текст джерелаvan der Zant, Tijn, Lambert Schomaker, and Edwin Valentijn. "Large scale parallel document image processing." In Electronic Imaging 2008, edited by Berrin A. Yanikoglu and Kathrin Berkner. SPIE, 2008. http://dx.doi.org/10.1117/12.765482.
Повний текст джерелаHeindl, Christoph, Gernot Stübl, Thomas Pönitz, Andreas Pichler, and Josef Scharinger. "Visual large-scale industrial interaction processing." In 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.
Повний текст джерелаIm, ChangJin, Jae-Heon Jeong, and Chang-Sung Jeong. "Parallel Large-Scale Image Processing for Orthorectification." In TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650289.
Повний текст джерелаPernpeintner, Reinhold, Angelika Fröhlich, and Thomas Lippert. "CFRP Infusion Processing on Large-Scale Cylindrica..." In 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.
Повний текст джерелаHerbst, Ludolf, and Jan Brune. "Micro-scale large-area UV laser processing." In SPIE LASE: Lasers and Applications in Science and Engineering, edited by Wilhelm Pfleging, Yongfeng Lu, Kunihiko Washio, Willem Hoving, and Jun Amako. SPIE, 2009. http://dx.doi.org/10.1117/12.807828.
Повний текст джерелаFilho, P. Souza, L. Felipe, P. Aragäo, L. Bejarano, D. Thomé de Paula, A. Sardinha, A. Azambuja, and F. Sierra. "Large Scale Seismic Processing in Public Cloud." In 82nd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202011916.
Повний текст джерелаЗвіти організацій з теми "Large Scale Processing"
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.
Повний текст джерелаWhiting, M. A. Object-oriented design: Deriving conceptual solutions to large-scale information processing problems. Office of Scientific and Technical Information (OSTI), May 1990. http://dx.doi.org/10.2172/6895465.
Повний текст джерелаBilgutay, Nihat M. Computer Facilities for High-Speed Data Acquisition, Signal Processing and Large Scale System Simulation. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada170935.
Повний текст джерелаGaliani, Sebastian, Ramiro Gálvez, and 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, June 2023. http://dx.doi.org/10.3386/w31295.
Повний текст джерелаHenz, Brian J., John Lazorisak, Jaroslaw Knap, Jason Livingston, and 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, September 2014. http://dx.doi.org/10.21236/ada610234.
Повний текст джерелаHobbs, D. T., D. Chai, D. Hartsough, and 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), September 1995. http://dx.doi.org/10.2172/607121.
Повний текст джерелаSalter, R., Quyen Dong, Cody Coleman, Maria Seale, Alicia Ruvinsky, LaKenya Walker, and W. Bond. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40203.
Повний текст джерелаThompson. L52208 Coating and Backfill System Optimisation. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2004. http://dx.doi.org/10.55274/r0010964.
Повний текст джерелаSIEGFRIED, MATTHEW, WILLIAM RAMSEY, and 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), November 2019. http://dx.doi.org/10.2172/1701701.
Повний текст джерелаEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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