Littérature scientifique sur le sujet « MapReduce programming model »
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Articles de revues sur le sujet "MapReduce programming model"
Zhang, Guigang, Chao Li, Yong Zhang et Chunxiao Xing. « A Semantic++ MapReduce Parallel Programming Model ». International Journal of Semantic Computing 08, no 03 (septembre 2014) : 279–99. http://dx.doi.org/10.1142/s1793351x14400091.
Texte intégralLämmel, Ralf. « Google’s MapReduce programming model — Revisited ». Science of Computer Programming 70, no 1 (janvier 2008) : 1–30. http://dx.doi.org/10.1016/j.scico.2007.07.001.
Texte intégralRetnowo, Murti. « Syncronize Data Using MapReduceModel Programming ». International Journal of Engineering Technology and Natural Sciences 3, no 2 (31 décembre 2021) : 82–88. http://dx.doi.org/10.46923/ijets.v3i2.140.
Texte intégralGarg, Uttama. « Data Analytic Models That Redress the Limitations of MapReduce ». International Journal of Web-Based Learning and Teaching Technologies 16, no 6 (novembre 2021) : 1–15. http://dx.doi.org/10.4018/ijwltt.20211101.oa7.
Texte intégralGao, Tilei, Ming Yang, Rong Jiang, Yu Li et 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.
Texte intégralSiddesh, G. M., Kavya Suresh, K. Y. Madhuri, Madhushree Nijagal, B. R. Rakshitha et K. G. Srinivasa. « Optimizing Crawler4j using MapReduce Programming Model ». Journal of The Institution of Engineers (India) : Series B 98, no 3 (12 août 2016) : 329–36. http://dx.doi.org/10.1007/s40031-016-0267-z.
Texte intégralZhang, Weidong, Boxin He, Yifeng Chen et Qifei Zhang. « GMR : graph-compatible MapReduce programming model ». Multimedia Tools and Applications 78, no 1 (23 août 2017) : 457–75. http://dx.doi.org/10.1007/s11042-017-5102-2.
Texte intégralDurairaj, M., et T. S. Poornappriya. « Importance of MapReduce for Big Data Applications : A Survey ». Asian Journal of Computer Science and Technology 7, no 1 (5 mai 2018) : 112–18. http://dx.doi.org/10.51983/ajcst-2018.7.1.1817.
Texte intégralRokhman, Nur, et Amelia Nursanti. « The MapReduce Model on Cascading Platform for Frequent Itemset Mining ». IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 12, no 2 (31 juillet 2018) : 149. http://dx.doi.org/10.22146/ijccs.34102.
Texte intégralWang, Changjian, Yuxing Peng, Mingxing Tang, Dongsheng Li, Shanshan Li et Pengfei You. « An Efficient MapReduce Computing Model for Imprecise Applications ». International Journal of Web Services Research 13, no 3 (juillet 2016) : 46–63. http://dx.doi.org/10.4018/ijwsr.2016070103.
Texte intégralThèses sur le sujet "MapReduce programming model"
Elteir, Marwa Khamis. « A MapReduce Framework for Heterogeneous Computing Architectures ». Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28786.
Texte intégralPh. D.
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.
Texte intégralOver the past two decades, owing to the reduction of storage, exchange and data processing costs, the volume of data generated each year continues to explode. The challenges related to big data processing are often described by the 3Vs : volume, variety and velocity of data creation, analysis, and sharing. To store and analyze these large datasets, it is essential to use clusters of machines and scalable algorithms that are insensitive to load imbalance that may occur among processing nodes. Applications such as collaborative filtering, deduplication and entity resolution are necessary to identify relationships in big datasets relying on a notion of similarity between records. These applications enable finding users with similar tastes, cleaning data and detecting frauds in large datasets. In these cases, similarity join and similarity search operations are often used to retrieve all similar records in one or more datasets using a distance and a user-defined threshold. Although parallel joins have been widely studied and successfully implemented on parallel and distributed architectures, the algorithms are not suitable for similarity join operation, since there is no hashing or sorting techniques, in the literature, that can retrieve all potentially similar record pairs in one step. Many techniques have been introduced in the literature to reduce the search space while ensuring the completeness of the result and avoiding the computation of a Cartesian product and the comparison of all record pairs. However, the scalability of these techniques is limited and does not allow efficient processing of big datasets on large-scale systems. Approximate methods have been proposed to handle similarity join by ignoring a very small part of the result and providing a probabilistic guarantee on the completeness of the results, while reducing the search space. These methods rely on hash functions whose collision probabilities are sensitive to the objects' similarity. We rely on these techniques to propose efficient solutions for similarity join and similarity search processing, based on LSH (Locality Sensitive Hashing), distributed histograms, and randomized communication schemes in order to reduce processing time, communication, and disks I/O costs to only relevant data for various distances and objects. The aim is to propose a generic framework based on the MapReduce programming model that meets the challenges of volume, variety, and velocity of big data analysis.The efficiency and scalability of the proposed solutions were studied using a cost model and confirmed by a series of experiments measuring the result completeness and the reduction of the search space, while guaranteeing efficient similarity join processing regardless the data size and data skew, the distance and the user-defined thresholds
Chen, Jhih-Siang, et 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.
Texte intégral淡江大學
資訊管理學系碩士班
104
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 distributed sequential pattern mining method based on Hadoop platform and Map Reduce programming model. Mining tasks are decomposed to many distributed tasks, the Map function is used to mine each sequential pattern in a subset of database. Then the Reduce function merges together all these identified patterns. It simplifies the search space and acquires a higher mining efficiency. In this study, we have further discussion on the influence of the setting of user-specified minimum support threshold on the distributed mining process. According to our experiments, it has been found that the threshold setting should be different in Map and Reduce mining process to prevent loss of some frequent patterns.
Chapitres de livres sur le sujet "MapReduce programming model"
Jin, Hai, Shadi Ibrahim, Li Qi, Haijun Cao, Song Wu et Xuanhua Shi. « The MapReduce Programming Model and Implementations ». Dans Cloud Computing, 373–90. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940105.ch14.
Texte intégralJin, Chao, et Rajkumar Buyya. « MapReduce Programming Model for .NET-Based Cloud Computing ». Dans Lecture Notes in Computer Science, 417–28. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03869-3_41.
Texte intégralJain, Arushi, Vishal Bhatnagar et Annavarapu Chandra Sekhara Rao. « Smart Heart Attack Forewarning Model Using MapReduce Programming Paradigm ». Dans Advances in Information Communication Technology and Computing, 37–43. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5421-6_5.
Texte intégralJanaki Meena, M., et S. P. Syed Ibrahim. « Statistical and Evolutionary Feature Selection Techniques Parallelized Using MapReduce Programming Model ». Dans Studies in Big Data, 159–80. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27520-8_8.
Texte intégralIndyk, Wojciech, Tomasz Kajdanowicz et Przemyslaw Kazienko. « Cooperative Decision Making Algorithm for Large Networks Using MapReduce Programming Model ». Dans Lecture Notes in Computer Science, 53–56. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32609-7_7.
Texte intégralBrindha, G. Siva, et M. Gobi. « CryptoDataMR : Enhancing the Data Protection Using Cryptographic Hash and Encryption/Decryption Through MapReduce Programming Model ». Dans International Conference on Innovative Computing and Communications, 95–115. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3315-0_9.
Texte intégralArputhamary, B. « Skew Handling Technique for Scheduling Huge Data Mapper with High End Reducers in MapReduce Programming Model ». Dans Learning and Analytics in Intelligent Systems, 331–39. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38501-9_33.
Texte intégralSuthaharan, Shan. « MapReduce Programming Platform ». Dans Machine Learning Models and Algorithms for Big Data Classification, 99–119. Boston, MA : Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_5.
Texte intégralDimitrov, Vladimir. « Cloud Programming Models (MapReduce) ». Dans Encyclopedia of Cloud Computing, 596–608. Chichester, UK : John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118821930.ch49.
Texte intégralRyczkowska, Magdalena, et Marek Nowicki. « Performance Comparison of Graph BFS Implemented in MapReduce and PGAS Programming Models ». Dans Parallel Processing and Applied Mathematics, 328–37. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78054-2_31.
Texte intégralActes de conférences sur le sujet "MapReduce programming model"
Ming, Li, Xu Guang-Hui, Wu Li-Fa et Ji Yao. « Performance Research on MapReduce Programming Model ». Dans 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC). IEEE, 2011. http://dx.doi.org/10.1109/imccc.2011.60.
Texte intégralSiddesh, G. M., K. G. Srinivasa, Ishank Mishra, Abhinav Anurag et Eklavya Uppal. « Phylogenetic Analysis Using MapReduce Programming Model ». Dans 2015 IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW). IEEE, 2015. http://dx.doi.org/10.1109/ipdpsw.2015.57.
Texte intégralLuo, Yuan, et Beth Plale. « Hierarchical MapReduce Programming Model and Scheduling Algorithms ». Dans 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2012. http://dx.doi.org/10.1109/ccgrid.2012.132.
Texte intégralBenelallam, Amine, Abel Gómez et Massimo Tisi. « ATL-MR : model transformation on MapReduce ». Dans SPLASH '15 : Conference on Systems, Programming, Languages, and Applications : Software for Humanity. New York, NY, USA : ACM, 2015. http://dx.doi.org/10.1145/2837476.2837482.
Texte intégralKang, Yun Hee, et Young B. Park. « Applying MapReduce Programming Model for Handling Scientific Problems ». Dans 2014 International Conference on Information Science and Applications (ICISA). IEEE, 2014. http://dx.doi.org/10.1109/icisa.2014.6847367.
Texte intégralZhao, Junfeng, Wenhui Gai et Han Wu. « Fortran Code Refactoring Based on MapReduce Programming Model ». Dans The 35th International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc., 2023. http://dx.doi.org/10.18293/seke2023-072.
Texte intégralLi, Min, Xin Yang et Xiaolin Li. « Domain-Based MapReduce Programming Model for Complex Scientific Applications ». Dans 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.
Texte intégralDeshmukh, Rajshree A., Bharathi H. N. et Amiya K. Tripathy. « Parallel Processing of Frequent Itemset Based on MapReduce Programming Model ». Dans 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA). IEEE, 2019. http://dx.doi.org/10.1109/iccubea47591.2019.9128369.
Texte intégralVione, Engelbertus, et J. B. Budi Darmawan. « Performance of K-means in Hadoop Using MapReduce Programming Model ». Dans International Conference of Science and Technology for the Internet of Things. EAI, 2019. http://dx.doi.org/10.4108/eai.19-10-2018.2282545.
Texte intégralZhang, Fan, Qutaibah M. Malluhi et Tamer M. Elsyed. « ConMR : Concurrent MapReduce Programming Model for Large Scale Shared-Data Applications ». Dans 2013 42nd International Conference on Parallel Processing (ICPP). IEEE, 2013. http://dx.doi.org/10.1109/icpp.2013.134.
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