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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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Proving Equivalence Between Imperative and MapReduce Implementations Using Program Transformations [PDF]
Electronic Proceedings in Theoretical Computer Science, 2018 Distributed programs are often formulated in popular functional frameworks like MapReduce, Spark and Thrill, but writing efficient algorithms for such frameworks is usually a non-trivial task.
Bernhard Beckert +5 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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M2M: A Simple Matlab-to-MapReduce Translator for Cloud Computing
Tsinghua Science and Technology, 2013 MapReduce is a very popular parallel programming model for cloud computing platforms, and has become an effective method for processing massive data by using a cluster of computers.
Junbo Zhang +3 more
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REST-MapReduce: An Integrated Interface but Differentiated Service
Journal of Applied Mathematics, 2014 With the fast deployment of cloud computing, MapReduce architectures are becoming the major technologies for mobile cloud computing. The concept of MapReduce was first introduced as a novel programming model and implementation for a large set of ...
Jong-Hyuk Park +3 more
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On using MapReduce to scale algorithms for Big Data analytics: a case study
Journal of Big Data, 2019 Introduction Many data analytics algorithms are originally designed for in-memory data. Parallel and distributed computing is a natural first remedy to scale these algorithms to “Big algorithms” for large-scale data.
Phongphun Kijsanayothin +2 more
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Cloudgene: A graphical execution platform for MapReduce programs on private and public clouds
BMC Bioinformatics, 2012 Background The MapReduce framework enables a scalable processing and analyzing of large datasets by distributing the computational load on connected computer nodes, referred to as a cluster.
Schönherr Sebastian +5 more
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