Results 11 to 20 of about 10,108 (225)
Procedia Computer Science, 2013 AbstractRecent innovations in Big Data have enabled major strides forward in our ability to glean important insights from massive amounts of data, and to use these insights to make better decisions. Underlying many of these innovations is a computational paradigm known as MapReduce, which enables computational processes to be scaled up to very large ...
Garcia, Christopher
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Accumulative Computation on MapReduce [PDF]
IPSJ Online Transactions, 2014 MapReduce programming model attracts a lot of enthusiasm among both industry and academia, largely because it simplifies the implementations of many data parallel applications.
Liu, Yu +3 more
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Research on Computing Efficiency of MapReduce in Big Data Environment [PDF]
ITM Web of Conferences, 2019 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.
Gao Tilei +4 more
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A Novel Configuration Tuning Method Based on Feature Selection for Hadoop MapReduce
IEEE Access, 2020 Configuration parameter optimization is an important means of improving the performance of the MapReduce model. The existing parameter tuning methods usually optimize all configuration parameters in MapReduce.
Jun Liu +4 more
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Journal of Applied Science and Engineering, 2022 Big data in healthcare defines a massive quantity of healthcare data accumulated from massive sources like electronic health records (EHR), medical imaging, genomic sequence, pharmacological research, wearable, medical gadgets, etc.
T. Gayathri, D. Lalitha Bhaskari
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XRepo 2.0: a Big Data Information System for Education in Prognostics and Health Management
International Journal of Prognostics and Health Management, 2021 Within Industry 4.0, Prognostics and Health Management (PHM) holds great potential due to its ability to bring deep insights into the current state of manufacturing equipment.
Nestor Romero +4 more
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Behavioral simulations in MapReduce [PDF]
Proceedings of the VLDB Endowment, 2010 In many scientific domains, researchers are turning to large-scale behavioral simulations to better understand real-world phenomena. While there has been a great deal of work on simulation tools from the high-performance computing community, behavioral simulations remain challenging to program and automatically scale in parallel environments.
Guozhang Wang +7 more
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Formal derivation of distributed MapReduce [PDF]
, 2014 MapReduce is a powerful distributed data processing model that is currently adopted in a wide range of domains to efficiently handle large volumes of data, i.e., cope with the big data surge.
Salehi Fathabadi, Asieh +9 more
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Garbage collection auto-tuning for Java MapReduce on Multi-Cores [PDF]
, 2011 MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational ...
Brown, G. +7 more
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Evaluation of high-level query languages based on MapReduce in Big Data
Journal of Big Data, 2018 MapReduce (MR) is a criterion of Big Data processing model with parallel and distributed large datasets. This model knows difficult problems related to low-level and batch nature of MR that gives rise to an abstraction layer on the top of MR.
Marouane Birjali +2 more
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