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Relevant bibliographies by topics / Amazon Elastic MapReduce

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Academic literature on the topic 'Amazon Elastic MapReduce'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Amazon Elastic MapReduce"

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Goncalves, Carlos, Luis Assuncao, and Jose C. Cunha. "Flexible MapReduce Workflows for Cloud Data Analytics." International Journal of Grid and High Performance Computing 5, no. 4 (2013): 48–64. http://dx.doi.org/10.4018/ijghpc.2013100104.

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Data analytics applications handle large data sets subject to multiple processing phases, some of which can execute in parallel on clusters, grids or clouds. Such applications can benefit from using MapReduce model, only requiring the end-user to define the application algorithms for input data processing and the map and reduce functions, but this poses a need to install/configure specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. In order to provide more flexibility in defining and adjusting the application configurations, as well as in the specification of the co

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Ankush, Verma* Dr Neelesh Jain. "AMAZON HADOOP FRAMEWORK USED IN BUSINESS FOR BIG DATA ANALYSIS." Global Journal of Engineering Science and Research Management 4, no. 5 (2017): 131–35. https://doi.org/10.5281/zenodo.801272.

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The Amazon MapReduce programming model, introduced by Amazon, a simple and efficient way of performing distributed computation over large data sets on the web especially for e-commerce. Amazon EMR work on Master/Slave Architecture using Amazon EMR for map and reduce big data. Amazon EC2 use cloud computing is a central part of designed web service that provides resizable compute capacity in the cloud. Here we also discuss about the Benefit and limitation of using Amazon EMR. Amazon S3 use easy to store and retrieve any amount of data on web. A Amazon clusters is a set of servers that work toge

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J S, Shyam Mohan, and Shanmugapriya P. "PROGRESSIVE DATA ANALYTICS IN HEALTH INFORMATICS USING AMAZON ELASTIC MAPREDUCE (EMR)." ICTACT Journal on Soft Computing 06, no. 03 (2016): 1218–23. http://dx.doi.org/10.21917/ijsc.2016.0168.

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Nellore, Abhinav, Christopher Wilks, Kasper D. Hansen, Jeffrey T. Leek, and Ben Langmead. "Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce." Bioinformatics 32, no. 16 (2016): 2551–53. http://dx.doi.org/10.1093/bioinformatics/btw177.

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Sharma, Tapan, Vinod Shokeen, and Sunil Mathur. "Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony." International Journal of Service Science, Management, Engineering, and Technology 11, no. 2 (2020): 80–94. http://dx.doi.org/10.4018/ijssmet.2020040105.

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The remote sensing domain has witnessed tremendous growth in the past decade, due to advancement in technology. In order to store and process such a large amount of data, a platform like Hadoop is leveraged. This article proposes a MapReduce (MR) approach to perform edge detection of satellite images using a nature-inspired algorithm Artificial Bee Colony (ABC). Edge detection is one of the significant steps in the field of image processing and is being used for object detection in the image. The article also compares two edge detection approaches on Hadoop with respect to scalability paramete

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Abbas, Haider Hadi, Poh Soon JosephNg, Ahmed Lateef Khalaf, Jamal Fadhil Tawfeq, and Ahmed Dheyaa Radhi. "A powerful heuristic method for generating efficient database systems." Bulletin of Electrical Engineering and Informatics 12, no. 6 (2023): 3706–16. http://dx.doi.org/10.11591/eei.v12i6.5070.

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Heuristic functions are an integral part of MapReduce software, both in Apache Hadoop and Spark. If the heuristic function performs badly, the load in the reduce part will not be balanced and access times spike. To investigate this problem closer, we run an optimal database program with numerous different heuristic functions on database. We will leverage the Amazon elastic MapReduce framework. The paper investigates on general purpose, implementation, and evaluation of heuristic algorithm for generating optimal database system, checksum, and special heuristic functions. With the analysis, we p

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Alaka, Hafiz A., Lukumon O. Oyedele, Hakeem A. Owolabi, Muhammad Bilal, Saheed O. Ajayi, and Olugbenga O. Akinade. "A framework for big data analytics approach to failure prediction of construction firms." Applied Computing and Informatics 16, no. 1/2 (2018): 207–22. http://dx.doi.org/10.1016/j.aci.2018.04.003.

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This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon El

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MacDonald, Graham, Alex Engler, Jeffrey Levy, and Sarah Armstrong. "Spark for Social Science." International Journal of Population Data Science 3, no. 5 (2018). http://dx.doi.org/10.23889/ijpds.v3i5.1044.

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Urban has developed an elastic and powerful approach to the analysis of massive datasets using Amazon Web Services’ Elastic MapReduce (EMR) and the Spark framework for distributed memory and processing. The goal of the project is to deliver powerful and elastic Spark clusters to researchers and data analysts with as little setup time and effort possible, and at low cost. To do that, at the Urban Institute, we use two critical components: (1) an Amazon Web Services (AWS) CloudFormation script to launch AWS Elastic MapReduce (EMR) clusters (2) a bootstrap script that runs on the Master node of t

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Al-Fatlawi, Ahmed Abdul Hassan, Ghassan N. Mohammed, and Israa Al Barazanchi. "Optimizing the Performance of Clouds Using Hash Codes in Apache Hadoop and Spark." Journal of Southwest Jiaotong University 54, no. 6 (2019). http://dx.doi.org/10.35741/issn.0258-2724.54.6.3.

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Hash functions are an integral part of MapReduce software, both in Apache Hadoop and Spark. If the hash function performs badly, the load in the reduced part will not be balanced and access times will spike. To investigate this problem further, we ran the Wordcount program with numerous different hash functions on Amazon AWS. In particular, we will leverage the Amazon Elastic MapReduce framework. The paper investigates the general purpose, cryptographic, checksum, and special hash functions. Through the analysis, we present the corresponding runtime results.

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Linderman, Michael D., Davin Chia, Forrest Wallace, and Frank A. Nothaft. "DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark." BMC Bioinformatics 20, no. 1 (2019). http://dx.doi.org/10.1186/s12859-019-3108-7.

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Abstract Background XHMM is a widely used tool for copy-number variant (CNV) discovery from whole exome sequencing data but can require hours to days to run for large cohorts. A more scalable implementation would reduce the need for specialized computational resources and enable increased exploration of the configuration parameter space to obtain the best possible results. Results DECA is a horizontally scalable implementation of the XHMM algorithm using the ADAM framework and Apache Spark that incorporates novel algorithmic optimizations to eliminate unneeded computation. DECA parallelizes XH

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Books on the topic "Amazon Elastic MapReduce"

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Singh, Amarkant, and Vijay Rayapati. Learning Big Data with Amazon Elastic MapReduce. Packt Publishing, Limited, 2014.

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Singh, Amarkant, and Vijay Rayapati. Learning Big Data with Amazon Elastic Mapreduce. Packt Publishing, Limited, 2014.

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Book chapters on the topic "Amazon Elastic MapReduce"

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Radenski Atanas and Norris Boyana. "MapReduce Streaming Algorithms for Laplace Relaxation on the Cloud." In Advances in Parallel Computing. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-381-0-215.

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A MapReduce (MR) technique known as MR message passing enables the development of distributed relaxation algorithms for the Laplace and Poisson equations. While a message-based MR relaxation solver can handle data grids in a fault-tolerant and scalable distributed execution, it also may generate a large number of messages to be routed from mapper to reducer tasks. The volume of intermediate data in the MR network can become a performance bottleneck for larger-scale grids and thus offset the benefits of distributed MR execution. In this paper, we introduce two optimizations, local in-mapper agg

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Conference papers on the topic "Amazon Elastic MapReduce"

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Hamdi, Hassen, Rim Zarrouk, Ramzi Mahmoudi, and Narjes Benameur. "Establishing an Interactive Virtual Library for Medical Manuscript Preservation Using KNN/SVM and an Amazon Elastic MapReduce Model." In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA). IEEE, 2024. https://doi.org/10.1109/aiccsa63423.2024.10912593.

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Nagaratna, M., and Y. Sowmya. "M-sanit: Computing misusability score and effective sanitization of big data using Amazon elastic MapReduce." In 2017 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC). IEEE, 2017. http://dx.doi.org/10.1109/iccpeic.2017.8290334.

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