Literatura académica sobre el tema "MAPREDUCE FRAMEWORKS"
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Artículos de revistas sobre el tema "MAPREDUCE FRAMEWORKS"
Ajibade Lukuman Saheed, Abu Bakar Kamalrulnizam, Ahmed Aliyu y Tasneem Darwish. "Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A Review". Systematic Literature Review and Meta-Analysis Journal 2, n.º 2 (19 de octubre de 2021): 53–60. http://dx.doi.org/10.54480/slrm.v2i2.19.
Texto completoDarapaneni, Chandra Sekhar, Bobba Basaveswara Rao, Boggavarapu Bhanu Venkata Satya Vara Prasad y Suneetha Bulla. "An Analytical Performance Evaluation of MapReduce Model Using Transient Queuing Model". Advances in Modelling and Analysis B 64, n.º 1-4 (31 de diciembre de 2021): 46–53. http://dx.doi.org/10.18280/ama_b.641-407.
Texto completoKang, Sol Ji, Sang Yeon Lee y Keon Myung Lee. "Performance Comparison of OpenMP, MPI, and MapReduce in Practical Problems". Advances in Multimedia 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/575687.
Texto completoSrirama, Satish Narayana, Oleg Batrashev, Pelle Jakovits y Eero Vainikko. "Scalability of Parallel Scientific Applications on the Cloud". Scientific Programming 19, n.º 2-3 (2011): 91–105. http://dx.doi.org/10.1155/2011/361854.
Texto completoSenthilkumar, M. y P. Ilango. "A Survey on Job Scheduling in Big Data". Cybernetics and Information Technologies 16, n.º 3 (1 de septiembre de 2016): 35–51. http://dx.doi.org/10.1515/cait-2016-0033.
Texto completoAdornes, Daniel, Dalvan Griebler, Cleverson Ledur y Luiz Gustavo Fernandes. "Coding Productivity in MapReduce Applications for Distributed and Shared Memory Architectures". International Journal of Software Engineering and Knowledge Engineering 25, n.º 09n10 (noviembre de 2015): 1739–41. http://dx.doi.org/10.1142/s0218194015710096.
Texto completoSong, Minjae, Hyunsuk Oh, Seungmin Seo y Kyong-Ho Lee. "Map-Side Join Processing of SPARQL Queries Based on Abstract RDF Data Filtering". Journal of Database Management 30, n.º 1 (enero de 2019): 22–40. http://dx.doi.org/10.4018/jdm.2019010102.
Texto completoThabtah, Fadi, Suhel Hammoud y Hussein Abdel-Jaber. "Parallel Associative Classification Data Mining Frameworks Based MapReduce". Parallel Processing Letters 25, n.º 02 (junio de 2015): 1550002. http://dx.doi.org/10.1142/s0129626415500024.
Texto completoGoncalves, Carlos, Luis Assuncao y Jose C. Cunha. "Flexible MapReduce Workflows for Cloud Data Analytics". International Journal of Grid and High Performance Computing 5, n.º 4 (octubre de 2013): 48–64. http://dx.doi.org/10.4018/ijghpc.2013100104.
Texto completoEsposito, Christian y Massimo Ficco. "Recent Developments on Security and Reliability in Large-Scale Data Processing with MapReduce". International Journal of Data Warehousing and Mining 12, n.º 1 (enero de 2016): 49–68. http://dx.doi.org/10.4018/ijdwm.2016010104.
Texto completoTesis sobre el tema "MAPREDUCE FRAMEWORKS"
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.
Texto completoThe need of analyzing large data sets from many different application fields has fostered the use of simplified programming models like MapReduce. Its current popularity is justified by being a useful abstraction to express data parallel processing and also by effectively hiding synchronization, fault tolerance and load balancing management details from the application developer. MapReduce frameworks have also been ported to multi-core and shared memory computer systems. These frameworks propose to dedicate a different computing CPU core for each map or reduce task to execute them concurrently. Also, Map and Reduce phases share a common data structure where main computations are applied. In this work we describe some limitations of current multi-core MapReduce frameworks. First, we describe the relevance of the data structure used to keep all input and intermediate data in memory. Current multi-core MapReduce frameworks are designed to keep all intermediate data in memory. When executing applications with large data input, the available memory becomes too small to store all framework intermediate data and there is a severe performance loss. We propose a memory management subsystem to allow intermediate data structures the processing of an unlimited amount of data by the use of a disk spilling mechanism. Also, we have implemented a way to manage concurrent access to disk of all threads participating in the computation. Finally, we have studied the effective use of the memory hierarchy by the data structures of the MapReduce frameworks and proposed a new implementation of partial MapReduce tasks to the input data set. The objective is to make a better use of the cache and to eliminate references to data blocks that are no longer in use. Our proposal was able to significantly reduce the main memory usage and improves the overall performance with the increasing of cache memory usage.
Kumaraswamy, Ravindranathan Krishnaraj. "Exploiting Heterogeneity in Distributed Software Frameworks". Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/64423.
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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/.
Texto completoPeddi, Sri Vijay Bharat. "Cloud Computing Frameworks for Food Recognition from Images". Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32450.
Texto completoElteir, Marwa Khamis. "A MapReduce Framework for Heterogeneous Computing Architectures". Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28786.
Texto completoPh. D.
Alkan, Sertan. "A Distributed Graph Mining Framework Based On Mapreduce". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611588/index.pdf.
Texto completoWang, Yongzhi. "Constructing Secure MapReduce Framework in Cloud-based Environment". FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2238.
Texto completoZhang, Yue. "A Workload Balanced MapReduce Framework on GPU Platforms". Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1450180042.
Texto completoRaja, Anitha. "A Coordination Framework for Deploying Hadoop MapReduce Jobs on Hadoop Cluster". Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196951.
Texto completoApache Hadoop är ett öppen källkods system som levererar pålitlig, skalbar och distribuerad användning. Hadoop tjänster hjälper med distribuerad data förvaring, bearbetning, åtkomst och trygghet. MapReduce är en viktig del av Hadoop system och är designad att bearbeta stora data mängder och även distribuerad i flera leder. MapReduce är använt extensivt inom bearbetning av strukturerad och ostrukturerad data i olika branscher bl. a e-handel, webbsökning, sociala medier och även vetenskapliga beräkningar. Förståelse av MapReduces arbetsbelastningar är viktiga att få förbättrad konfigurationer och resultat. Men, arbetsbelastningar av MapReduce inom massproduktions miljö var inte djup-forskat hittills. I detta examensarbete, är en hel del fokus satt på ”Hadoop cluster” (som en utförande miljö i data bearbetning) att analysera två typer av Hadoop MapReduce (MR) arbeten genom ett tilltänkt system. Detta system är refererad som arbetsbelastnings översättare. Resultaten från denna arbete innehåller: (1) en parametrisk arbetsbelastningsmodell till inriktad MR arbeten, (2) en specifikation att utveckla förbättrad kluster strategier med båda modellen och koordinations system, och (3) förbättrad planering och arbetsprestationer, d.v.s kortare tid att utföra arbetet. Vi har realiserat en prototyp med Apache Tomcat på (OpenStack) Ubuntu Trusty Tahr som använder RESTful API (1) att skapa ”Hadoop cluster” version 2.7.2 och (2) att båda skala upp och ner antal medarbetare i kluster. Forskningens resultat har visat att med vältrimmad parametrar, kan MR arbete nå förbättringar dvs. sparad tid vid slutfört arbete och förbättrad användning av hårdvara resurser. Målgruppen för denna avhandling är utvecklare. I framtiden, föreslår vi tilläggning av olika parametrar att utveckla en allmän modell för MR och liknande arbeten.
Lakkimsetti, Praveen Kumar. "A framework for automatic optimization of MapReduce programs based on job parameter configurations". Kansas State University, 2011. http://hdl.handle.net/2097/12011.
Texto completoDepartment of Computing and Information Sciences
Mitchell L. Neilsen
Recently, cost-effective and timely processing of large datasets has been playing an important role in the success of many enterprises and the scientific computing community. Two promising trends ensure that applications will be able to deal with ever increasing data volumes: first, the emergence of cloud computing, which provides transparent access to a large number of processing, storage and networking resources; and second, the development of the MapReduce programming model, which provides a high-level abstraction for data-intensive computing. MapReduce has been widely used for large-scale data analysis in the Cloud [5]. The system is well recognized for its elastic scalability and fine-grained fault tolerance. However, even to run a single program in a MapReduce framework, a number of tuning parameters have to be set by users or system administrators to increase the efficiency of the program. Users often run into performance problems because they are unaware of how to set these parameters, or because they don't even know that these parameters exist. With MapReduce being a relatively new technology, it is not easy to find qualified administrators [4]. The major objective of this project is to provide a framework that optimizes MapReduce programs that run on large datasets. This is done by executing the MapReduce program on a part of the dataset using stored parameter combinations and setting the program with the most efficient combination and this modified program can be executed over the different datasets. We know that many MapReduce programs are used over and over again in applications like daily weather analysis, log analysis, daily report generation etc. So, once the parameter combination is set, it can be used on a number of data sets efficiently. This feature can go a long way towards improving the productivity of users who lack the skills to optimize programs themselves due to lack of familiarity with MapReduce or with the data being processed.
Capítulos de libros sobre el tema "MAPREDUCE FRAMEWORKS"
Singh, Jaspreet, S. N. Panda y Rajesh Kaushal. "Performance Evaluation of Big Data Frameworks: MapReduce and Spark". En Advances in Intelligent Systems and Computing, 1611–19. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5903-2_167.
Texto completoDhanani, Jenish, Rupa Mehta, Dipti Rana y Bharat Tidke. "Back-Propagated Neural Network on MapReduce Frameworks: A Survey". En Smart Innovations in Communication and Computational Sciences, 381–91. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2414-7_35.
Texto completoNoh, Hyunho y Jun-Ki Min. "An Efficient Data Access Method Exploiting Quadtrees on MapReduce Frameworks". En Database Systems for Advanced Applications, 86–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40270-8_8.
Texto completoSalto, Carolina, Gabriela Minetti, Enrique Alba y Gabriel Luque. "Developing Genetic Algorithms Using Different MapReduce Frameworks: MPI vs. Hadoop". En Advances in Artificial Intelligence, 262–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00374-6_25.
Texto completoXu, Huanle, Ronghai Yang, Zhibo Yang y Wing Cheong Lau. "Solving Large Graph Problems in MapReduce-Like Frameworks via Optimized Parameter Configuration". En Algorithms and Architectures for Parallel Processing, 525–39. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27122-4_36.
Texto completoReinders, James, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook y Xinmin Tian. "Common Parallel Patterns". En Data Parallel C++, 323–52. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5574-2_14.
Texto completoJeyaraj, Rathinaraja, Ganeshkumar Pugalendhi y Anand Paul. "Hadoop Framework". En Big Data with Hadoop MapReduce, 47–111. Includes bibliographical references and index.: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429321733-2.
Texto completoSuryawanshi, Sahiba y Praveen Kaushik. "Efficient MapReduce Framework Using Summation". En Data, Engineering and Applications, 3–11. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6351-1_1.
Texto completoCho, Kyung Soo, Ji Yeon Lim, Jae Yeol Yoon, Young Hee Kim, Seung Kwan Kim y Ung Mo Kim. "Opinion Mining in MapReduce Framework". En Communications in Computer and Information Science, 50–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22365-5_7.
Texto completoLiu, Xiufeng, Christian Thomsen y Torben Bach Pedersen. "The ETLMR MapReduce-Based ETL Framework". En Lecture Notes in Computer Science, 586–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22351-8_48.
Texto completoActas de conferencias sobre el tema "MAPREDUCE FRAMEWORKS"
Lee, Haejoon, Minseo Kang, Sun-Bum Youn, Jae-Gil Lee y YongChul Kwon. "An Experimental Comparison of Iterative MapReduce Frameworks". En CIKM'16: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2983323.2983647.
Texto completoWang, Haoyu, Haiying Shen, Charles Reiss, Arnim Jain y Yunqiao Zhang. "Improved Intermediate Data Management for MapReduce Frameworks". En 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2020. http://dx.doi.org/10.1109/ipdps47924.2020.00062.
Texto completoHsaini, Sara, Salma Azzouzi y My El Hassan Charaf. "A Secure Testing Based Approach for Mapreduce Frameworks". En 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). IEEE, 2018. http://dx.doi.org/10.1109/icecocs.2018.8610596.
Texto completoJakovits, Pelle y Satish Narayana Srirama. "Evaluating MapReduce frameworks for iterative Scientific Computing applications". En 2014 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2014. http://dx.doi.org/10.1109/hpcsim.2014.6903690.
Texto completoHaque, Ahsanul y Latifur Khan. "MapReduce Based Frameworks for Classifying Evolving Data Stream". En 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013. http://dx.doi.org/10.1109/icdmw.2013.145.
Texto completoAhmad, Maaz Bin Safeer y Alvin Cheung. "Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications". En SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183713.3196891.
Texto completoTrong-Tuan Vu y Fabrice Huet. "A Lightweight Continuous Jobs Mechanism for MapReduce Frameworks". En 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2013. http://dx.doi.org/10.1109/ccgrid.2013.36.
Texto completoGhit, Bogdan y Dick Epema. "Tyrex: Size-Based Resource Allocation in MapReduce Frameworks". En 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2016. http://dx.doi.org/10.1109/ccgrid.2016.82.
Texto completoRivas-Gomez, Sergio, Stefano Markidis, Erwin Laure, Keeran Brabazon, Oliver Perks y Sai Narasimhamurthy. "Decoupled Strategy for Imbalanced Workloads in MapReduce Frameworks". En 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2018. http://dx.doi.org/10.1109/hpcc/smartcity/dss.2018.00153.
Texto completoGuo, Jia y Gagan Agrawal. "Achieving Performance and Programmability for MapReduce(-Like) Frameworks". En 2018 IEEE 25th International Conference on High Performance Computing (HiPC). IEEE, 2018. http://dx.doi.org/10.1109/hipc.2018.00043.
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