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Procedia Computer Science, 2012 13 pages, appeared at ICCS ...
Matthew Felice Pace +1 more
exaly +4 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015 MapReduce is a commonly used framework for executing data-intensive jobs on distributed server clusters. We introduce a variant implementation of MapReduce, namely "Coded MapReduce", to substantially reduce the inter-server communication load for the shuffling phase of MapReduce, and thus accelerating its execution.
Songze Li +2 more
openaire +2 more sources