Bibliografías temáticas / MapReduce programming model

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Literatura académica sobre el tema "MapReduce programming model"

Autor: Grafiati
Publicado: 12 de octubre de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "MapReduce programming model"

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Zhang, Guigang, Chao Li, Yong Zhang, and Chunxiao Xing. "A Semantic++ MapReduce Parallel Programming Model." International Journal of Semantic Computing 08, no. 03 (2014): 279–99. http://dx.doi.org/10.1142/s1793351x14400091.

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Big data is playing a more and more important role in every area such as medical health, internet finance, culture and education etc. How to process these big data efficiently is a huge challenge. MapReduce is a good parallel programming language to process big data. However, it has lots of shortcomings. For example, it cannot process complex computing. It cannot suit real-time computing. In order to overcome these shortcomings of MapReduce and its variants, in this paper, we propose a Semantic++ MapReduce parallel programming model. This study includes the following parts. (1) Semantic++ MapR
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Lämmel, Ralf. "Google’s MapReduce programming model — Revisited." Science of Computer Programming 70, no. 1 (2008): 1–30. http://dx.doi.org/10.1016/j.scico.2007.07.001.

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Retnowo, Murti. "Syncronize Data Using MapReduceModel Programming." International Journal of Engineering Technology and Natural Sciences 3, no. 2 (2021): 82–88. http://dx.doi.org/10.46923/ijets.v3i2.140.

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Research in the processing of the data shows that the larger data increasingly requires a longer time. Processing huge amounts of data on a single computer has limitations that can be overcome by parallel processing. This study utilized the MapReduce programming model data synchronization by duplicating the data from database client to database server. MapReduce is a programming model that was developed to speed up the processing of large data. MapReduce model application on the training process performed on data sharing that is adapted to number of sub-process (thread) and data entry to datab
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Garg, Uttama. "Data Analytic Models That Redress the Limitations of MapReduce." International Journal of Web-Based Learning and Teaching Technologies 16, no. 6 (2021): 1–15. http://dx.doi.org/10.4018/ijwltt.20211101.oa7.

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The amount of data in today’s world is increasing exponentially. Effectively analyzing Big Data is a very complex task. The MapReduce programming model created by Google in 2004 revolutionized the big-data comput-ing market. Nowadays the model is being used by many for scientific and research analysis as well as for commercial purposes. The MapReduce model however is quite a low-level progamming model and has many limitations. Active research is being undertaken to make models that overcome/remove these limitations. In this paper we have studied some popular data analytic models that redress s
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Gao, Tilei, Ming Yang, Rong Jiang, Yu Li, and Yao Yao. "Research on Computing Efficiency of MapReduce in Big Data Environment." ITM Web of Conferences 26 (2019): 03002. http://dx.doi.org/10.1051/itmconf/20192603002.

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The emergence of big data has brought a great impact on traditional computing mode, the distributed computing framework represented by MapReduce has become an important solution to this problem. Based on the big data, this paper deeply studies the principle and framework of MapReduce programming. On the basis of mastering the principle and framework of MapReduce programming, the time consumption of distributed computing framework MapReduce and traditional computing model is compared with concrete programming experiments. The experiment shows that MapReduce has great advantages in large data vo
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Siddesh, G. M., Kavya Suresh, K. Y. Madhuri, Madhushree Nijagal, B. R. Rakshitha, and K. G. Srinivasa. "Optimizing Crawler4j using MapReduce Programming Model." Journal of The Institution of Engineers (India): Series B 98, no. 3 (2016): 329–36. http://dx.doi.org/10.1007/s40031-016-0267-z.

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Zhang, Weidong, Boxin He, Yifeng Chen, and Qifei Zhang. "GMR: graph-compatible MapReduce programming model." Multimedia Tools and Applications 78, no. 1 (2017): 457–75. http://dx.doi.org/10.1007/s11042-017-5102-2.

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Durairaj, M., and T. S. Poornappriya. "Importance of MapReduce for Big Data Applications: A Survey." Asian Journal of Computer Science and Technology 7, no. 1 (2018): 112–18. http://dx.doi.org/10.51983/ajcst-2018.7.1.1817.

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Significant regard for MapReduce framework has been trapped by a wide range of areas. It is presently a practical model for data-focused applications because of its basic interface of programming, high elasticity, and capacity to withstand the subjection to defects. Additionally, it is fit for preparing a high extent of data in Distributed Computing environments (DCE). MapReduce, on various events, has turned out to be material to a wide scope of areas. MapReduce is a parallel programming model and a related usage presented by Google. In the programming model, a client determines the calculati
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Charanjeet, Kaur*1& Sumanpreet Kaur2. "NOVEL IMPROVED CAPACITY SCHEDULING ALGORITHM FOR HETEROGENEOUS HADOOP." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 401–10. https://doi.org/10.5281/zenodo.814540.

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For large scale parallel applications Mapreduce is a widely used programming model. Mapreduce is an important programming model for parallel applications. Hadoop is a open source which is popular for developing data based applications and hadoop is a open source implementation of Mapreduce. Mapreduce gives programming interfaces to share data based in a cluster or distributed environment. As it works in a distributed environment so it should provide efficient scheduling mechanisms for efficient work capability in distributed environment. locality and synchronization overhead are main issues in
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Rokhman, Nur, and Amelia Nursanti. "The MapReduce Model on Cascading Platform for Frequent Itemset Mining." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 12, no. 2 (2018): 149. http://dx.doi.org/10.22146/ijccs.34102.

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The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data. This paper discusses the implementation
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Tesis sobre el tema "MapReduce programming model"

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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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Rivault, Sébastien. "Parallélisme, équilibrage de charges et extensibilité dans le traitement des mégadonnées sur des systèmes à grande échelle." Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1019.

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Durant les deux dernières décennies, grâce à la réduction des coûts de stockage, d'échange et de traitement de l'information, le volume de données générées chaque année ne cesse d'exploser. Les enjeux liés au traitement de ces mégadonnées sont souvent décrits par la règle des 3V : le volume, la variété et la vitesse de création, de collecte, d'analyse et de partage des données. Pour stocker et analyser ces ensembles de données volumineux, il est essentiel d'utiliser des grappes de machines et des algorithmes extensibles et insensibles aux déséquilibres pouvant se produire pour répartir équitab
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Chen, Jhih-Siang, and 陳智翔. "A study of distributed sequential pattern mining method based on MapReduce programming model." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/18996078478404490541.

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碩士<br>淡江大學<br>資訊管理學系碩士班<br>104<br>Sequential pattern mining is a data mining method for obtaining frequent sequential patterns in a large sequential database. Conventional sequence data mining methods could be divided into two categories: Apriori-like methods and pattern growth methods. These algorithms are mainly executed on standalone environment. There are some disadvantages like large database scanning time, scalability problem, less efficient for massive dataset. To improve the performance of sequential pattern mining and to improve the scalability issues, this study presents a distribute
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Capítulos de libros sobre el tema "MapReduce programming model"

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Jin, Hai, Shadi Ibrahim, Li Qi, Haijun Cao, Song Wu, and Xuanhua Shi. "The MapReduce Programming Model and Implementations." In Cloud Computing. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940105.ch14.

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Jin, Chao, and Rajkumar Buyya. "MapReduce Programming Model for .NET-Based Cloud Computing." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03869-3_41.

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Jain, Arushi, Vishal Bhatnagar, and Annavarapu Chandra Sekhara Rao. "Smart Heart Attack Forewarning Model Using MapReduce Programming Paradigm." In Advances in Information Communication Technology and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5421-6_5.

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Janaki Meena, M., and S. P. Syed Ibrahim. "Statistical and Evolutionary Feature Selection Techniques Parallelized Using MapReduce Programming Model." In Studies in Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27520-8_8.

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Indyk, Wojciech, Tomasz Kajdanowicz, and Przemyslaw Kazienko. "Cooperative Decision Making Algorithm for Large Networks Using MapReduce Programming Model." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32609-7_7.

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Brindha, G. Siva, and M. Gobi. "CryptoDataMR: Enhancing the Data Protection Using Cryptographic Hash and Encryption/Decryption Through MapReduce Programming Model." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3315-0_9.

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Arputhamary, B. "Skew Handling Technique for Scheduling Huge Data Mapper with High End Reducers in MapReduce Programming Model." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38501-9_33.

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Suthaharan, Shan. "MapReduce Programming Platform." In Machine Learning Models and Algorithms for Big Data Classification. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_5.

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Dimitrov, Vladimir. "Cloud Programming Models (MapReduce)." In Encyclopedia of Cloud Computing. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118821930.ch49.

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Ryczkowska, Magdalena, and Marek Nowicki. "Performance Comparison of Graph BFS Implemented in MapReduce and PGAS Programming Models." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78054-2_31.

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Actas de conferencias sobre el tema "MapReduce programming model"

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Semwal, Akshita, Anirudh Purohit, Pallava Joshi, Manisha Basera, Vihan Singh Bhakuni, and Manika Manwal. "Performance Evaluation of K-Means Clustering Using MapReduce Programming Model." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841066.

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Ming, Li, Xu Guang-Hui, Wu Li-Fa, and Ji Yao. "Performance Research on MapReduce Programming Model." In 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC). IEEE, 2011. http://dx.doi.org/10.1109/imccc.2011.60.

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Siddesh, G. M., K. G. Srinivasa, Ishank Mishra, Abhinav Anurag, and Eklavya Uppal. "Phylogenetic Analysis Using MapReduce Programming Model." In 2015 IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW). IEEE, 2015. http://dx.doi.org/10.1109/ipdpsw.2015.57.

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Luo, Yuan, and Beth Plale. "Hierarchical MapReduce Programming Model and Scheduling Algorithms." In 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2012. http://dx.doi.org/10.1109/ccgrid.2012.132.

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Benelallam, Amine, Abel Gómez, and Massimo Tisi. "ATL-MR: model transformation on MapReduce." In SPLASH '15: Conference on Systems, Programming, Languages, and Applications: Software for Humanity. ACM, 2015. http://dx.doi.org/10.1145/2837476.2837482.

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Kang, Yun Hee, and Young B. Park. "Applying MapReduce Programming Model for Handling Scientific Problems." In 2014 International Conference on Information Science and Applications (ICISA). IEEE, 2014. http://dx.doi.org/10.1109/icisa.2014.6847367.

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Zhao, Junfeng, Wenhui Gai, and Han Wu. "Fortran Code Refactoring Based on MapReduce Programming Model." In The 35th International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc., 2023. http://dx.doi.org/10.18293/seke2023-072.

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Li, Min, Xin Yang, and Xiaolin Li. "Domain-Based MapReduce Programming Model for Complex Scientific Applications." In 2013 IEEE International Conference on High Performance Computing and Communications (HPCC) & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2013. http://dx.doi.org/10.1109/hpcc.and.euc.2013.87.

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Deshmukh, Rajshree A., Bharathi H. N., and Amiya K. Tripathy. "Parallel Processing of Frequent Itemset Based on MapReduce Programming Model." In 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA). IEEE, 2019. http://dx.doi.org/10.1109/iccubea47591.2019.9128369.

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Vione, Engelbertus, and J. B. Budi Darmawan. "Performance of K-means in Hadoop Using MapReduce Programming Model." In International Conference of Science and Technology for the Internet of Things. EAI, 2019. http://dx.doi.org/10.4108/eai.19-10-2018.2282545.

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